├── Analysis_B0005.ipynb ├── Dataset ├── B0005_discharge.csv ├── B0051_2_charge.csv ├── B0051_2_discharge.csv ├── B0051_2_impedance.csv ├── Coll_Cap_B56718.csv ├── Data Description └── README.md ├── Documents ├── Analysis.ipynb └── literature review.ipynb ├── Final_Capstone.ipynb ├── LICENSE ├── LSTM_Paper ├── DSTLM_Paper.ipynb ├── DSTLM_Paper_4.1.ipynb ├── Discharge5_H1.csv ├── Discharge6_H1.csv ├── LSTM_paper_2.ipynb ├── LTSM_Paper_P2.ipynb ├── Window size5 │ ├── TIEDVD_B005_WindowSize5.ipynb │ ├── TIEDVD_B006_WindowSize5.ipynb │ └── log └── log ├── Last_modification ├── Real_Time.ipynb ├── Result │ ├── B0005 │ │ ├── SP101-1.png │ │ ├── SP101-2.png │ │ ├── SP101-3.png │ │ ├── SP101-4.png │ │ ├── SP101-5.png │ │ ├── SP101-6.png │ │ ├── SP110-1.png │ │ ├── SP110-2.png │ │ ├── SP110-3.png │ │ ├── SP110-4.png │ │ ├── SP110-5.png │ │ ├── SP80-1.png │ │ ├── SP80-2.png │ │ ├── SP80-3.png │ │ ├── SP80-4.png │ │ ├── SP80-5.png │ │ ├── SP80-6.png │ │ ├── SP90-1.png │ │ ├── SP90-2.png │ │ ├── SP90-3.png │ │ ├── SP90-4.png │ │ ├── SP90-5.png │ │ └── log │ └── log └── log ├── ML.ipynb ├── Online ├── Cycle100-w5.png ├── Cycle110-w5.png ├── Cycle50-w10.png ├── Cycle80-w5.png ├── LSTM_Capacity_online.ipynb └── log ├── Online_Cap ├── B0006_on.csv ├── Coulomb_counting_SOC.ipynb ├── LTSM_Capacity_online.ipynb └── log ├── PythonCode ├── New_Cap_online.py ├── STLM_RUL_Multi └── STLM_multi.py ├── README.md ├── Real_Time_Pred ├── LSTM_Capacity_all_iter.ipynb ├── LSTM_Capacity_online.ipynb ├── Untitled Diagram.png └── log ├── STLM_onevar_multiSteps.ipynb ├── log └── map.ipynb /Dataset/B0005_discharge.csv: -------------------------------------------------------------------------------- 1 | Batt_name,cycle,dateTime,time,Capacity 2 | B0005_10,1,24,3690.234,1.85648742 3 | B0005_10,2,24,3672.344,1.84632725 4 | B0005_10,3,24,3651.641,1.83534919 5 | B0005_10,4,24,3631.563,1.83526253 6 | B0005_10,5,24,3629.172,1.83464551 7 | B0005_10,6,24,3652.281,1.83566166 8 | B0005_10,7,24,3650.828,1.83514614 9 | B0005_10,8,24,3572.453,1.82575679 10 | B0005_10,9,24,3550.594,1.82477385 11 | B0005_10,10,24,3551.25,1.82461327 12 | B0005_10,11,24,3530.25,1.82461955 13 | B0005_10,12,24,3491.016,1.81420194 14 | B0005_10,13,24,3470.281,1.81375216 15 | B0005_10,14,24,3470,1.81344049 16 | B0005_10,15,24,3450.297,1.802598 17 | B0005_10,16,24,3429.407,1.8021069 18 | B0005_10,17,24,3409.484,1.8025795 19 | B0005_10,18,24,3390.422,1.80306831 20 | B0005_10,19,24,3390.281,1.80277762 21 | B0005_10,20,24,3591.734,1.84702599 22 | B0005_10,21,24,3552.297,1.84741731 23 | B0005_10,22,24,3531.578,1.83617742 24 | B0005_10,23,24,3492.907,1.82578075 25 | B0005_10,24,24,3511.594,1.82511364 26 | B0005_10,25,24,3431.93700000001,1.8255815 27 | B0005_10,26,24,3431.125,1.81403113 28 | B0005_10,27,24,3412.406,1.81476919 29 | B0005_10,28,24,3410.688,1.81396939 30 | B0005_10,29,24,3410.718,1.80276567 31 | B0005_10,30,24,3392.719,1.80407704 32 | B0005_10,31,24,3470.672,1.85180255 33 | B0005_10,32,24,3414.109,1.83070385 34 | B0005_10,33,24,3395.219,1.81990411 35 | B0005_10,34,24,3386,1.80930796 36 | B0005_10,35,24,3368.187,1.8046099 37 | B0005_10,36,24,3368.266,1.79937707 38 | B0005_10,37,24,3358.719,1.78844323 39 | B0005_10,38,24,3339.5,1.78292305 40 | B0005_10,39,24,3331.265,1.77303372 41 | B0005_10,40,24,3321.188,1.77303776 42 | B0005_10,41,24,3321.844,1.76787211 43 | B0005_10,42,24,3311.828,1.76231507 44 | B0005_10,43,24,3327.391,1.76761729 45 | B0005_10,44,24,3312.078,1.76266836 46 | B0005_10,45,24,3293.172,1.75173049 47 | B0005_10,46,24,3275.328,1.7418496 48 | B0005_10,47,24,3265.016,1.73609135 49 | B0005_10,48,24,3328.937,1.79362401 50 | B0005_10,49,24,3329.266,1.78318902 51 | B0005_10,50,24,3301.579,1.76736421 52 | B0005_10,51,24,3283.563,1.75701779 53 | B0005_10,52,24,3275.125,1.74687062 54 | B0005_10,53,24,3265.656,1.74171725 55 | B0005_10,54,24,3266.093,1.73642251 56 | B0005_10,55,24,3257.594,1.72632172 57 | B0005_10,56,24,3248.593,1.71580654 58 | B0005_10,57,24,3238.766,1.71053335 59 | B0005_10,58,24,3240.14,1.7060145 60 | B0005_10,59,24,3230.109,1.70031103 61 | B0005_10,60,24,3229.781,1.69457986 62 | B0005_10,61,24,3212.469,1.68490291 63 | B0005_10,62,24,3213.219,1.67447416 64 | B0005_10,63,24,3203.578,1.67456925 65 | B0005_10,64,24,3195.125,1.66371638 66 | B0005_10,65,24,3195.31299999999,1.65901387 67 | B0005_10,66,24,3186.297,1.65385406 68 | B0005_10,67,24,3176.438,1.64265378 69 | B0005_10,68,24,3167.593,1.63785784 70 | B0005_10,69,24,3167.719,1.63273504 71 | B0005_10,70,24,3159.07799999999,1.62775289 72 | B0005_10,71,24,3148.829,1.62212549 73 | B0005_10,72,24,3149.094,1.61132566 74 | B0005_10,73,24,3140.547,1.60656314 75 | B0005_10,74,24,3131.625,1.60151422 76 | B0005_10,75,24,3121.907,1.59036923 77 | B0005_10,76,24,3122.125,1.585789 78 | B0005_10,77,24,3121.672,1.58494307 79 | B0005_10,78,24,3120.86,1.59552639 80 | B0005_10,79,24,3103.828,1.57473017 81 | B0005_10,80,24,3095.094,1.564902 82 | B0005_10,81,24,3095.781,1.55976595 83 | B0005_10,82,24,3094.328,1.55948157 84 | B0005_10,83,24,3086.531,1.55468935 85 | B0005_10,84,24,3085.625,1.54887411 86 | B0005_10,85,24,3067.203,1.5382366 87 | B0005_10,86,24,3058.157,1.52791426 88 | B0005_10,87,24,3058.812,1.52852526 89 | B0005_10,88,24,3058.578,1.52264733 90 | B0005_10,89,24,3049.328,1.51748599 91 | B0005_10,90,24,3177.219,1.6058189 92 | B0005_10,91,24,3082.937,1.56384914 93 | B0005_10,92,24,3074.266,1.54809152 94 | B0005_10,93,24,3066,1.53237556 95 | B0005_10,94,24,3056.437,1.52695283 96 | B0005_10,95,24,3048.219,1.5169573 97 | B0005_10,96,24,3039.218,1.5118976 98 | B0005_10,97,24,3039.047,1.5065639 99 | B0005_10,98,24,3030.375,1.50154535 100 | B0005_10,99,24,3021,1.49084441 101 | B0005_10,100,24,3021.75,1.48586838 102 | B0005_10,101,24,3012.265,1.48041368 103 | B0005_10,102,24,3002.641,1.47520959 104 | B0005_10,103,24,3012,1.4859036 105 | B0005_10,104,24,3020.11,1.49609183 106 | B0005_10,105,24,3002.17200000001,1.48080059 107 | B0005_10,106,24,2992.703,1.46975433 108 | B0005_10,107,24,2964.468,1.45390123 109 | B0005_10,108,24,2983.375,1.45414621 110 | B0005_10,109,24,2975.297,1.45502375 111 | B0005_10,110,24,2974.985,1.44904216 112 | B0005_10,111,24,2966.25,1.43867094 113 | B0005_10,112,24,2956.359,1.43344543 114 | B0005_10,113,24,2956.344,1.43339589 115 | B0005_10,114,24,2956.406,1.42806487 116 | B0005_10,115,24,2947.266,1.42292032 117 | B0005_10,116,24,2937.984,1.41742903 118 | B0005_10,117,24,2929.125,1.41240923 119 | B0005_10,118,24,2928.454,1.41257879 120 | B0005_10,119,24,2929.375,1.40759837 121 | B0005_10,120,24,2946.031,1.43339205 122 | B0005_10,121,24,2955.438,1.43825502 123 | B0005_10,122,24,2937.625,1.41735472 124 | B0005_10,123,24,2929.078,1.40698166 125 | B0005_10,124,24,2918.375,1.40120378 126 | B0005_10,125,24,2919.812,1.39670082 127 | B0005_10,126,24,2900.422,1.39128479 128 | B0005_10,127,24,2901.375,1.38622877 129 | B0005_10,128,24,2901.187,1.38043668 130 | B0005_10,129,24,2891.64,1.37523642 131 | B0005_10,130,24,2882.906,1.3705128 132 | B0005_10,131,24,2883.265,1.37050856 133 | B0005_10,132,24,2882.969,1.36473551 134 | B0005_10,133,24,2891.656,1.37539202 135 | B0005_10,134,24,2901.25,1.38611152 136 | B0005_10,135,24,2891.703,1.36985 137 | B0005_10,136,24,2883.016,1.36478279 138 | B0005_10,137,24,2873.453,1.35432205 139 | B0005_10,138,24,2874.031,1.3546417 140 | B0005_10,139,24,2874.234,1.35470392 141 | B0005_10,140,24,2864.984,1.349315 142 | B0005_10,141,24,2855.093,1.34418919 143 | B0005_10,142,24,2855.625,1.33899138 144 | B0005_10,143,24,2855.594,1.33891452 145 | B0005_10,144,24,2847.437,1.33400668 146 | B0005_10,145,24,2847.36,1.32864443 147 | B0005_10,146,24,2837.328,1.3231709 148 | B0005_10,147,24,2837.281,1.31816916 149 | B0005_10,148,24,2838.515,1.31846644 150 | B0005_10,149,24,2828.328,1.31829304 151 | B0005_10,150,24,2838.265,1.32387242 152 | B0005_10,151,24,2864.547,1.36012168 153 | B0005_10,152,24,2846.39,1.33953069 154 | B0005_10,153,24,2837.969,1.32902866 155 | B0005_10,154,24,2838.156,1.32367413 156 | B0005_10,155,24,2829.843,1.3186339 157 | B0005_10,156,24,2829.625,1.31347513 158 | B0005_10,157,24,2819.437,1.31320206 159 | B0005_10,158,24,2819.579,1.30779599 160 | B0005_10,159,24,2810.672,1.30303292 161 | B0005_10,160,24,2811.485,1.30335736 162 | B0005_10,161,24,2802.422,1.30341004 163 | B0005_10,162,24,2801.625,1.29788708 164 | B0005_10,163,24,2802.031,1.29807351 165 | B0005_10,164,24,2793.39,1.29346361 166 | B0005_10,165,24,2793.39,1.28800339 167 | B0005_10,166,24,2792.485,1.28745252 168 | B0005_10,167,24,2802.016,1.30901536 169 | B0005_10,168,24,2820.39,1.32507933 170 | -------------------------------------------------------------------------------- /Dataset/Data Description: -------------------------------------------------------------------------------- 1 | Data Description: 2 | The data Li-ion battery (#51) were run through 3 different operational profiles (charge, discharge and impedance) 3 | at an ambient temperature of 4 deg C. Charging was carried out in a constant current (CC) mode at 1.5A until the battery 4 | voltage reached 4.2V and then continued in a constant voltage (CV) mode until the charge current dropped to 20mA. 5 | 6 | Fixed load current level of 2A was used and the discharge runs were stopped at 2V, 2.2V, 2.5V and 2.7V for battery 51. 7 | Impedance measurement was carried out through an electrochemical impedance spectroscopy (EIS) frequency sweep 8 | from 0.1Hz to 5kHz. The experiments were carried out until the experiment control software crashed. 9 | 10 | Note that there are several discharge runs where the capacity as well as voltage levels were very low. Reasons for this 11 | have not been fully analyzed. 12 | -------------------------------------------------------------------------------- /Dataset/README.md: -------------------------------------------------------------------------------- 1 | Modification: 2 | 3 | The Dataset updated on 02/21/2019 by adding Capacity field on Discharge dataset and correct the cycle number. 4 | 5 | 6 | 02/21/2019 at 3:00 AM we add a new dataset call B0005_discharge.csv for B0005, every one cycle represent as one row. 7 | 8 | 02/23/2019 at 23:00, create a new dataset combination of charge and discharge, every cycle starts with charge operation and completed with discharge operation. 9 | 10 | 03/10/2019 at 13:30, reading papers about (RUL) 11 | 12 | 03/28/2019 at 11:00 AM started build different type of models to predict RUL (Classical ML). 13 | 14 | 04/04/2019 at 11:00 AM, Start build LSTM model to predict RUL. 15 | 16 | -------------------------------------------------------------------------------- /Documents/literature review.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\n", 8 | "

Data-driven prognostic method based on Bayesian approaches for direct remaining useful life (RUL)

\n", 9 | "

A. Mosallam · K. Medjaher · N. Zerhouni \n", 10 | "\n", 11 | " \n", 12 | " \n", 13 | "\n", 14 | " They used two phases data driven method for RUL prediction: \n", 15 | " \n", 16 | "1- Offline phase: The proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. \n", 17 | " \n", 18 | "\n", 19 | "2- Online phase: the method finds the most similar offline health indicator, to the online health indicator, using k-nearest neighbors (k-NN) classifier to use it as a RUL predictor. \n", 20 | "\n", 21 | "\n", 22 | "The method finally estimates the degradation state using discrete Bayesian filter. \n", 23 | "\n", 24 | "\n", 25 | "Dataset: they applied the offline and online phases to predict RUL on (charge and discharge) dataset.\n", 26 | "

\n", 27 | " " 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "

Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

\n", 35 | "\n", 36 | "Bhaskar Saha, Member, IEEE, Kai Goebel, Scott Poll, and Jon Christophersen \n", 37 | "\n", 38 | " \n", 39 | "

\n", 40 | "In this paper, Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data. By using a Bayesian learning (RVM–PF) framework to encapsulate the randomness of RUL and improve its prediction with increasing information (measurements). \n", 41 | "\n", 42 | "

  • Impedance dataset was used, Re and Rct and Capacity were used to calculate SoC, SoH and EoL.

    • \n", 43 | "\n", 44 | "
    \n", 45 | "\n", 46 | " " 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "

    A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm

    \n", 54 | "\n", 55 | "Phattara Khumprom and Nita Yodo \n", 56 | "\n", 57 | "https://www.mdpi.com/1996-1073/12/4/660/htm#B3-energies-12-00660 \n", 58 | "\n", 59 | "

    \n", 60 | "This paper presents the preliminary development of the data-driven prognostic, using a Deep Neural Networks (DNN) approach to predict the SoH and the RUL of the lithium-ion battery. They compared their proposed with different machine learning (ML), namely, SVM, K-NN, ANN, LR. \n", 61 | "The experimental result shows that DNN get better result or outperformed compared with other ML the are proposed.

    \n", 62 | "
    " 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "

    Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

    \n", 70 | "\n", 71 | "Cunsong Wang ,Ningyun Lu ,Senlin Wang ,Yuehua Cheng andBin Jiang \n", 72 | "

    \n", 73 | "This paper they proposed a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis. \n", 74 | "\n", 75 | "The contribution of this proposal was: \n", 76 | "\n", 77 | "1- measured discharge voltages, a novel polynomial-fitting-based HI is constructed to replace Li-ion capacity for Li-ion battery RUL prognosis. \n", 78 | "\n", 79 | "2- Considering on-line and multi-step prediction, a DLSTM with the Adam method, L2 regularization method, and incremental learning idea was proposed.\n", 80 | "

    " 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "

    A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

    \n", 88 | "\n", 89 | "Meru A. Patil, Piyush Tagade, Krishnan S. Hariharan, Subramanya M. Kolake, Taewon Song, Taejung Yeo, Seokgwang Doo \n", 90 | "

    \n", 91 | "In this paper a novel method for real-time RUL estimation of Li ion batteries is proposed that integrates classification and regression attributes of Support Vector (SV) based machine learning technique. \n", 92 | "\n", 93 | "The contribution of this works was to design an efficient two-stage RUL estimation system that can predict the remaining life of a battery at any stage of its life, this work done by using SVM in both stages. \n", 94 | "\n", 95 | "Stage one: Classification model \n", 96 | "\n", 97 | "For each battery, total number of discharge cycles are counted till it reaches EOL and each discharge cycle is classified into four categories, namely class A, B, C and D. \n", 98 | "\n", 99 | "Stage two: Regression mode: \n", 100 | "\n", 101 | "The regression model is used for accurate RUL estimation of the battery data classified as ‘‘class D''.\n", 102 | "

    " 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "

    Data-driven Prognostics for Lithium-ion Battery Based on Gaussian Process Regression

    \n", 110 | "\n", 111 | "Datong Liu, Jingyue Pang, Jianbao Zhou, Yu Peng \n", 112 | "\n", 113 | "

    \n", 114 | "\n", 115 | "This paper presents the Gaussian process model to realize the prognostics for battery health. \n", 116 | "\n", 117 | "The contribution was to propose a new combination of covariance function for GPR to make prognostics about battery health and estimate the state of health about the battery. \n", 118 | "\n", 119 | "Based on GPR algorithm the experimental results prove its effectiveness and confirm the algorithm can be effectively applied to the battery monitoring and prognostics. \n", 120 | "\n", 121 | "The applied GPR only one battery B0005, so the GPR did not provide better results with less learning data. So, they present a dynamic data model to update the training inputs while the new data are available online to achieve more accurate prediction results.\n", 122 | "

    " 123 | ] 124 | }, 125 | { 126 | "cell_type": "markdown", 127 | "metadata": {}, 128 | "source": [ 129 | "

    Dynamic Battery Remaining Useful Life Estimation: An On-line Data-driven Approach \n", 130 | "

    \n", 131 | "Jianbao Zhou, Datong Liu, Yu Peng, Xiyuan Peng \n", 132 | "

    \n", 133 | "This paper proposed an online data driven battery RUL prediction approach based on Online Support Vector Regression (Online SVR). \n", 134 | "\n", 135 | "For estimation RUL: A data-driven battery RUL prediction strategy combined the offline historical models and on-line dynamic model realizes more precisely prediction result. \n", 136 | "\n", 137 | "Experimental results with the NASA battery data show that the proposed method can effectively predict the RUL of lithium battery. \n", 138 | "

    " 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "

    Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use

    \n", 146 | "\n", 147 | "Brian Bole, Chetan S. Kulkarni, Matthew Daigle \n", 148 | "

    \n", 149 | "This paper presents the online adaptation of battery models and proposed a novel method for the adaptation of parameters in an electrochemical model of a lithium-ion battery. And applied unscented Kalman filtering algorithm to enable the production of internal battery state estimates and age-dependent electrochemical model parameter estimates. The data was collected over randomized discharge profiles. This work used only discharge cycles to judge battery health. \n", 150 | "\n", 151 | "The experimental results presented compare online model estimates produced by unscented Kalman filtering algorithm to offline model estimates obtained by periodically taking batteries offline to run reference discharge cycles.\n", 152 | "\n", 153 | "

    \n" 154 | ] 155 | }, 156 | { 157 | "cell_type": "markdown", 158 | "metadata": {}, 159 | "source": [ 160 | "

    Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature

    \n", 161 | "\n", 162 | "Datong Liu, Yue Luo, Yu Peng, Xiyuan Peng, Michael Pecht5 \n", 163 | "

    \n", 164 | "the paper proposed an improved AR time series model called model Nonlinear Degradation Auto-Regression (ND-AR) for Remaining Useful Life (RUL) estimation of lithium-ion batteries. \n", 165 | "\n", 166 | "The nonlinear degradation to analyze feature of the lithium-ion battery capacity degradation and then the non-linear accelerated degradation factor is extracted to improve the linear AR model. \n", 167 | "\n", 168 | "Experimental results with the battery data set (from the CALCE of University of Maryland) show that the proposed nonlinear degradation AR model can realize satisfied prognostic for various lithium-ion batteries with low computing complexity.

    " 169 | ] 170 | }, 171 | { 172 | "cell_type": "markdown", 173 | "metadata": {}, 174 | "source": [] 175 | } 176 | ], 177 | "metadata": { 178 | "kernelspec": { 179 | "display_name": "Python 3", 180 | "language": "python", 181 | "name": "python3" 182 | }, 183 | "language_info": { 184 | "codemirror_mode": { 185 | "name": "ipython", 186 | "version": 3 187 | }, 188 | "file_extension": ".py", 189 | "mimetype": "text/x-python", 190 | "name": "python", 191 | "nbconvert_exporter": "python", 192 | "pygments_lexer": "ipython3", 193 | "version": "3.7.1" 194 | } 195 | }, 196 | "nbformat": 4, 197 | "nbformat_minor": 2 198 | } 199 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Khaled Alrfou, University of Wisconsin-Milwaukee. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /LSTM_Paper/Discharge5_H1.csv: -------------------------------------------------------------------------------- 1 | ,Batt_name,cycle,dateTime,amb_temp,voltage_battery,current_battery,temp_battery,voltage_load,current_load,time,Capacity,H 2 | 0,B0005_10,1,2008-04-02 15:25:41,24,3.277169976825196,-0.006528351251606992,34.230852841540965,0.0,-0.0006,3690.2340000000004,1.85648742,2363.438 3 | 1,B0005_10,2,2008-04-02 19:43:48,24,3.3002448871222545,-0.0004475525791171869,34.392136587188695,0.0,-0.0006,3672.344,1.84632725,2384.922 4 | 2,B0005_10,3,2008-04-03 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01:21:56,24,3.51004183648376,-0.0017631427961074745,36.50230588330288,0.0,0.0006,3074.2659999999996,1.54809152,1751.719 94 | 92,B0005_10,93,2008-05-10 06:18:12,24,3.5359247595765018,0.0015697237962090213,35.910172206779144,0.0,0.0006,3066.0,1.53237556,1732.9209999999998 95 | 93,B0005_10,94,2008-05-10 11:16:04,24,3.5293508069521957,-0.0039701157301653975,35.8789724790072,0.0,0.0006,3056.437,1.52695283,1714.203 96 | 94,B0005_10,95,2008-05-10 16:11:56,24,3.5418570338179896,-0.001085866637949761,35.62922817025395,0.0,0.0006,3048.219,1.5169573,1686.1399999999999 97 | 95,B0005_10,96,2008-05-10 21:07:54,24,3.540101144510816,-0.0017455531501402901,35.75119139564094,0.0,0.0006,3039.218,1.5118976,1676.891 98 | 96,B0005_10,97,2008-05-11 02:05:21,24,3.545308234960168,-0.0014086197388525627,35.64438752174055,0.0,0.0006,3039.047,1.5065639,1667.39 99 | 97,B0005_10,98,2008-05-11 06:55:27,24,3.543070358154518,-0.002012131901541318,35.71781750151811,0.0,0.0006,3030.375,1.50154535,1658.3909999999998 100 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23:53:17,24,3.544628258601667,-0.002622952553637643,36.14370701766192,0.0,0.0006,3002.172000000006,1.48080059,1611.3600000000065 107 | 105,B0005_10,106,2008-05-13 04:35:25,24,3.556359815036754,-0.0012078322390314845,35.81782913450557,0.0,0.0008,2992.703,1.46975433,1592.0 108 | 106,B0005_10,107,2008-05-13 09:24:04,24,3.5574151609154883,-0.0033190716918418185,35.64502538652201,0.0,0.0006,2964.468,1.45390123,1573.7350000000001 109 | 107,B0005_10,108,2008-05-13 14:20:24,24,3.5703941423271512,0.001559667277703501,35.390978396371295,0.0,0.0008,2983.375,1.45414621,1563.937 110 | 108,B0005_10,109,2008-05-13 19:04:20,24,3.557697563432477,-0.00160633340225061,35.90172424296188,0.0,0.0008,2975.297,1.45502375,1555.688 111 | 109,B0005_10,110,2008-05-13 23:59:26,24,3.5621145720736376,-0.002522692741776227,35.87657254499655,0.0,0.0006,2974.985000000001,1.44904216,1555.4379999999944 112 | 110,B0005_10,111,2008-05-14 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123,B0005_10,124,2008-05-17 12:21:15,24,3.5821071034262646,-0.0008830152641400449,35.08313938846722,0.0,0.0006,2918.375,1.40120378,1451.36 126 | 124,B0005_10,125,2008-05-17 17:15:37,24,3.5845442528815368,0.0008061978411406779,34.98209371471252,0.0,0.0006,2919.812,1.39670082,1443.031 127 | 125,B0005_10,126,2008-05-17 22:10:51,24,3.5829970635240485,-0.0013093723544827366,35.08471913955869,0.0,0.0006,2900.422,1.39128479,1424.141 128 | 126,B0005_10,127,2008-05-18 03:05:19,24,3.585576080990165,-0.0017033444156140732,34.94908275625882,0.0,0.0006,2901.375,1.38622877,1423.953 129 | 127,B0005_10,128,2008-05-18 08:00:50,24,3.59109083848252,0.0008809845830174383,34.70452784918293,0.0,0.0006,2901.187,1.38043668,1404.782 130 | 128,B0005_10,129,2008-05-18 12:44:48,24,3.594060583971402,-0.0026243009944540924,34.667131314804806,0.0,0.0006,2891.64,1.37523642,1395.516 131 | 129,B0005_10,130,2008-05-18 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170 | -------------------------------------------------------------------------------- /LSTM_Paper/Discharge6_H1.csv: -------------------------------------------------------------------------------- 1 | ,Batt_name,cycle,dateTime,amb_temp,voltage_battery,current_battery,temp_battery,voltage_load,current_load,time,Capacity,H 2 | 0,B0006_11,1,2008-04-02 15:25:41,24,2.4757677568281857,-2.0094358918725432,39.16298653238707,1.653,-1.999,3690.2340000000004,2.03533759,2636.094 3 | 1,B0006_11,2,2008-04-02 19:43:48,24,2.35152551190002,-2.010374855522972,39.2462026826329,1.5430000000000001,-1.999,3672.344,2.02514025,2620.2659999999996 4 | 2,B0006_11,3,2008-04-03 00:01:06,24,2.440479715106544,-2.0085588871101128,38.99920246025285,1.65,-1.999,3651.6409999999996,2.01332637,2620.656 5 | 3,B0006_11,4,2008-04-03 04:16:37,24,2.479155853900392,-2.0092904478622464,38.84362777186652,1.69,-1.999,3631.563,2.01328467,2620.875 6 | 4,B0006_11,5,2008-04-03 08:33:25,24,2.280187848173442,-2.011760840220058,38.97798888258284,1.4880000000000002,-1.999,3629.172,2.00052834,2599.516 7 | 5,B0006_11,6,2008-04-03 12:55:10,24,2.4392842568466446,-2.0109470452247566,38.83989920742278,1.659,-1.999,3652.281,2.01389908,2585.375 8 | 6,B0006_11,7,2008-04-03 17:17:16,24,2.2812068449406446,-2.012014626739587,39.04610837498471,1.505,-1.9992,3650.828,2.01310111,2584.7969999999996 9 | 7,B0006_11,8,2008-04-03 21:28:14,24,2.3664105270296134,-2.009602654155728,38.87507521022778,1.6019999999999999,-1.9992,3572.453,1.96878983,2563.657 10 | 8,B0006_11,9,2008-04-04 01:38:15,24,2.4360200495787083,-2.010503181695025,38.7260539826455,1.6640000000000001,-1.999,3550.594,1.96816618,2542.7340000000004 11 | 9,B0006_11,10,2008-04-04 05:48:08,24,2.1864377487643725,-2.0120006291945924,38.98629728904917,1.411,-1.999,3551.25,1.95723079,2523.532 12 | 10,B0006_11,11,2008-04-04 09:57:19,24,2.3406023605912454,-2.0086057639408743,38.659788619196426,1.5830000000000002,-1.9992,3530.25,1.94559915,2524.156 13 | 11,B0006_11,12,2008-04-04 17:56:27,24,2.4263021374140443,-2.008093198845442,38.84979195732924,1.659,-1.999,3491.0159999999996,1.9347505,2485.39 14 | 12,B0006_11,13,2008-04-04 22:01:54,24,2.475369770000704,-2.008861764489536,38.751283440151035,1.704,-1.999,3470.281,1.92327995,2465.563 15 | 13,B0006_11,14,2008-04-05 02:20:26,24,2.342364523655007,-2.0114258804383267,38.768537640587894,1.59,-1.999,3470.0,1.91188993,2446.094 16 | 14,B0006_11,15,2008-04-05 06:25:01,24,2.188400081882664,-2.0094401780170927,38.846861123443475,1.433,-1.999,3450.297,1.90106671,2427.172 17 | 15,B0006_11,16,2008-04-05 10:30:32,24,2.1206980945954057,-2.00629337660368,38.8658897816528,1.371,-1.999,3429.407,1.88919899,2404.9839999999995 18 | 16,B0006_11,17,2008-04-05 14:34:41,24,2.3699814577992986,-2.0083999032211324,38.647578733807656,1.625,-1.999,3409.4840000000004,1.87827837,2386.594 19 | 17,B0006_11,18,2008-04-05 18:39:25,24,2.247991649924625,-2.0103074874707247,38.79218188173866,1.524,-1.999,3390.422,1.86756972,2367.844 20 | 18,B0006_11,19,2008-04-05 22:46:35,24,2.8844463698113665,-0.002277241069617877,38.770222239295286,0.0,-0.0008,3390.281,1.86758926,2348.234 21 | 19,B0006_11,20,2008-04-18 21:10:19,24,2.203355846859377,-2.0099114516038865,37.54320599748628,1.445,-1.9992,3591.7340000000004,1.97962655,2564.218 22 | 20,B0006_11,21,2008-04-19 02:29:09,24,2.35887751155593,-2.010023846843136,37.90362577530197,1.619,-1.999,3552.297,1.95754991,2545.828 23 | 21,B0006_11,22,2008-04-19 07:47:15,24,2.218672914041916,-2.010621300102956,37.670160513988705,1.4909999999999999,-1.999,3531.578,1.94581502,2506.734 24 | 22,B0006_11,23,2008-04-19 13:01:42,24,2.339723057947995,-2.009836812236101,37.34166959188387,1.607,-1.999,3492.907,1.92424611,2486.203 25 | 23,B0006_11,24,2008-04-19 18:27:29,24,2.399750820894273,-2.008549110682199,37.28979017254232,1.653,-1.999,3511.594,1.94598113,2468.219 26 | 24,B0006_11,25,2008-04-19 23:41:55,24,2.491410879472442,-2.011253162152561,37.16794481484507,1.723,-1.999,3431.937000000005,1.90140793,2408.312999999997 27 | 25,B0006_11,26,2008-04-20 04:54:41,24,2.7669269890921613,-0.0013483637694533895,37.51577848501052,0.0,-0.0008,3431.125,1.87867421,2387.312 28 | 26,B0006_11,27,2008-04-20 10:16:28,24,2.8356128467177317,-0.0009996515493716173,37.24165964092258,0.0,-0.0006,3412.406,1.86830613,2369.016 29 | 27,B0006_11,28,2008-04-20 15:37:05,24,2.928394635151109,-0.003474005762546325,37.10097467364957,0.0,-0.0008,3410.688,1.85602441,2347.016 30 | 28,B0006_11,29,2008-04-20 20:50:38,24,3.0771149575987566,-0.0065849518216069824,36.53635083668188,0.0,-0.0006,3410.718,1.8451998,2326.125 31 | 29,B0006_11,30,2008-04-21 02:15:02,24,2.819560586845831,0.0017843402447845864,37.496077698962544,0.0,-0.0008,3392.719,1.85722889,2293.469 32 | 30,B0006_11,31,2008-04-22 15:33:49,24,2.434300686181212,-2.0101843550935627,37.250073961181094,1.607,1.999,3470.672,1.92477609,2407.609 33 | 31,B0006_11,32,2008-04-22 20:26:18,24,2.9187757391682454,0.00021994616902196602,37.27669935857522,0.0,0.0006,3414.1090000000004,1.88223175,2342.218 34 | 32,B0006_11,33,2008-04-23 01:18:11,24,2.973956908852585,-0.003806456592501386,37.053468915649404,0.0,0.0006,3395.219,1.85527669,2304.203 35 | 33,B0006_11,34,2008-04-23 06:18:19,24,3.0334333724088656,0.00115949398581303,36.49019770341733,0.0,0.0006,3386.0,1.83925089,2257.594 36 | 34,B0006_11,35,2008-04-23 11:08:00,24,3.0616925140357725,-0.0024706162055099685,36.17351619989374,0.0,0.0006,3368.187,1.81853961,2211.422 37 | 35,B0006_11,36,2008-04-23 16:06:25,24,3.0864214480405137,-0.002485656828321589,36.691721740616366,0.0,0.0006,3368.2659999999996,1.81347701,2201.875 38 | 36,B0006_11,37,2008-04-23 21:02:58,24,3.1377873218245425,-0.002908291707230441,36.30505451544784,0.0,0.0006,3358.719,1.79710185,2164.079 39 | 37,B0006_11,38,2008-04-24 01:51:33,24,3.1501156392374963,-0.0018286245424696968,36.06439554288408,0.0,0.0008,3339.5,1.78100463,2135.922 40 | 38,B0006_11,39,2008-04-24 06:50:00,24,3.1738920422499453,-0.008825733551338297,36.01731400053337,0.0,0.0006,3331.265,1.77101304,2089.391 41 | 39,B0006_11,40,2008-04-24 11:49:24,24,3.1972071497430035,-0.003438126514516957,35.90775442895192,0.0,0.0006,3321.188,1.76047124,2079.594 42 | 40,B0006_11,41,2008-04-24 16:45:56,24,3.227568458837217,-0.00492719060429448,36.35807936248104,0.0,0.0006,3321.844,1.75029138,2070.375 43 | 41,B0006_11,42,2008-04-24 21:38:11,24,3.2308580821504327,-0.001453011628706404,36.50421625915163,0.0,0.0006,3311.828,1.7394924999999999,2032.516 44 | 42,B0006_11,43,2008-04-25 12:03:44,24,3.199633267433362,-0.0022925624123046186,36.991264831075426,0.0,-0.0008,3327.3909999999996,1.75467666,2073.25 45 | 43,B0006_11,44,2008-04-25 20:03:55,24,3.1898103242112006,-0.002874522115121392,36.81410785577819,-0.0,0.0006,3312.078,1.76593909,2052.015 46 | 44,B0006_11,45,2008-04-26 01:04:35,24,3.2499622126804124,-0.004430988718560468,35.94866550135389,0.0,0.0006,3293.172,1.73378874,1994.859 47 | 45,B0006_11,46,2008-04-26 05:57:35,24,3.276178621777498,-0.0028865069759319247,35.80761414587771,0.0,0.0006,3275.328,1.71332591,1957.625 48 | 46,B0006_11,47,2008-04-26 10:58:25,24,3.2892337937863383,-0.001482135856283325,35.727411052391915,0.0,0.0008,3265.0159999999996,1.70240778,1919.766 49 | 47,B0006_11,48,2008-04-29 12:15:56,24,2.934951679828825,-0.0026359580742833216,39.57378670033301,0.0,0.0008,3328.937,1.82371946,2266.907 50 | 48,B0006_11,49,2008-04-29 17:05:27,24,3.0133583509832325,-0.0025141196723349147,38.781959990934574,-0.0,0.0008,3329.2659999999996,1.80812841,2182.797 51 | 49,B0006_11,50,2008-04-29 22:00:04,24,3.0981920097302105,0.0006501043390256764,37.8590470128016,0.0,0.0006,3301.579,1.77576109,2098.4539999999997 52 | 50,B0006_11,51,2008-04-30 02:43:55,24,3.167346834112308,0.0011884652184539464,37.12135762318553,0.0,0.0006,3283.563,1.74474098,2051.703 53 | 51,B0006_11,52,2008-04-30 07:41:42,24,3.2286855117160864,-0.0035102794129954827,36.580486706224,0.0,0.0006,3275.125,1.72920703,1986.484 54 | 52,B0006_11,53,2008-04-30 12:28:49,24,3.2595724548548413,-0.0012542411110933905,36.436407503512335,0.0,0.0006,3265.656,1.70810866,1948.25 55 | 53,B0006_11,54,2008-04-30 17:20:16,24,3.2774199521442657,-0.0024642794163139943,36.53091042103418,0.0,0.0006,3266.093,1.69759542,1929.547 56 | 54,B0006_11,55,2008-04-30 22:14:54,24,3.309541910489555,-0.0037357037492950548,36.06449507955633,0.0,0.0006,3257.594,1.68206345,1873.828 57 | 55,B0006_11,56,2008-05-01 03:15:33,24,3.3229457962953464,-0.004205019176247785,35.94327250944611,0.0,0.0008,3248.593,1.67147683,1836.484 58 | 56,B0006_11,57,2008-05-01 08:16:03,24,3.3268774953647884,-0.0045774706794028865,35.89295328706184,0.0,0.0006,3238.7659999999996,1.66075914,1807.484 59 | 57,B0006_11,58,2008-05-01 13:14:24,24,3.336400100467817,-0.004220549753628036,35.959515024229205,0.0,0.0008,3240.14,1.65112102,1780.063 60 | 58,B0006_11,59,2008-05-01 18:11:10,24,3.3562313241233728,-0.004630300153985874,36.03733181811758,0.0,0.0008,3230.1090000000004,1.64013493,1751.734 61 | 59,B0006_11,60,2008-05-01 23:10:15,24,3.371466361383496,0.0014743883145177995,35.71327174023829,0.0,0.0006,3229.781000000003,1.62919994,1732.6090000000002 62 | 60,B0006_11,61,2008-05-02 03:53:34,24,3.3960187616989215,-0.004672744022970511,35.366498347395265,0.0,0.0006,3212.469,1.60884963,1695.75 63 | 61,B0006_11,62,2008-05-02 08:44:40,24,3.393882521860081,-0.004211278055821824,35.18665272384657,0.0,0.0006,3213.219,1.60352449,1657.844 64 | 62,B0006_11,63,2008-05-02 13:43:05,24,3.3978158698372107,-0.00442104201360729,35.67614711684021,-0.0,0.0008,3203.578,1.59897121,1648.609 65 | 63,B0006_11,64,2008-05-02 19:07:48,24,3.416423032732188,-0.0003200027950143793,34.71660828900691,0.0,0.0008,3195.125,1.58762731,1592.4679999999998 66 | 64,B0006_11,65,2008-05-02 23:24:17,24,3.417549528683359,-0.001444462241441726,35.028736379559405,0.0,0.0006,3195.3129999999946,1.58297197,1593.1409999999998 67 | 65,B0006_11,66,2008-05-03 04:13:08,24,3.4275053861483777,-0.0034826648553054514,34.774991305740066,0.0,0.0006,3186.297,1.56713764,1565.078 68 | 66,B0006_11,67,2008-05-03 09:10:19,24,3.4347508426334845,-0.0044826484110488144,34.69789797592927,0.0,0.0006,3176.438,1.56134461,1536.1879999999999 69 | 67,B0006_11,68,2008-05-03 14:04:34,24,3.4481980199249085,-0.0012195271207717936,34.6005415728579,0.0,0.0006,3167.593,1.55117119,1508.4379999999999 70 | 68,B0006_11,69,2008-05-03 18:58:13,24,3.4471363584937484,0.0007226343450961433,34.655186314766375,0.0,0.0008,3167.719,1.54067416,1498.766 71 | 69,B0006_11,70,2008-05-03 23:42:30,24,3.4551154405959217,-0.0019271431845461406,34.49661503681511,0.0,0.0006,3159.077999999994,1.52531387,1479.9840000000063 72 | 70,B0006_11,71,2008-05-04 04:41:40,24,3.468023005179232,-0.0012882276547055275,34.58789396630877,0.0,0.0006,3148.829,1.53015703,1461.031 73 | 71,B0006_11,72,2008-05-04 09:40:11,24,3.4711335972831856,-0.00449031192608329,34.48391218889425,0.0,0.0006,3149.094,1.52465668,1442.3909999999998 74 | 72,B0006_11,73,2008-05-04 14:38:13,24,3.4883390886919767,-0.001221569208727667,34.32316829732572,0.0,0.0008,3140.547,1.5145493,1414.625 75 | 73,B0006_11,74,2008-05-04 19:37:07,24,3.4882264404681207,-9.1970080813463e-05,34.371124137441896,0.0,0.0006,3131.625,1.5093915,1405.391 76 | 74,B0006_11,75,2008-05-05 00:35:53,24,3.4877002346746275,-0.0025462031829215142,34.37262003693859,0.0,0.0006,3121.907,1.50399743,1386.156 77 | 75,B0006_11,76,2008-05-05 05:28:13,24,3.5019144212894115,-0.0018050512867516227,34.06737352776938,0.0,0.0006,3122.125,1.49352019,1367.5929999999998 78 | 76,B0006_11,77,2008-05-05 11:41:31,24,3.5064898836985074,-0.002346995765496664,34.01427816460032,0.0,0.0006,3121.672,1.4876323,1348.531 79 | 77,B0006_11,78,2008-05-05 20:40:08,24,3.4612712556588887,-0.0015893640732856102,35.633900290416996,0.0,0.0008,3120.86,1.53028762,1423.812 80 | 78,B0006_11,79,2008-05-06 01:38:46,24,3.49597974716647,-0.002813062156713118,34.63193513275843,0.0,0.0006,3103.828,1.50407601,1367.7350000000001 81 | 79,B0006_11,80,2008-05-06 06:37:06,24,3.4958682327661417,-0.002245547841319663,34.48366479877386,0.0,0.0006,3095.094,1.48875936,1330.625 82 | 80,B0006_11,81,2008-05-06 11:25:21,24,3.5168324777654205,-0.003048177774026196,34.198906360730284,0.0,0.0006,3095.781,1.47827793,1311.734 83 | 81,B0006_11,82,2008-05-06 16:23:36,24,3.5140805590225344,-0.003030933482684179,34.73440888809496,0.0,0.0006,3094.328,1.47845998,1311.375 84 | 82,B0006_11,83,2008-05-06 21:21:49,24,3.5179169265872945,-0.0031973699811009533,34.854516068350875,0.0,0.0006,3086.531,1.47321484,1311.907 85 | 83,B0006_11,84,2008-05-07 02:17:25,24,3.5191016758451497,-0.002832656974994741,34.71454754205853,0.0,0.0006,3085.625,1.46751649,1283.25 86 | 84,B0006_11,85,2008-05-07 07:04:02,24,3.521493643197122,-0.0023048413012049227,34.408441964409874,0.0,0.0006,3067.203,1.45162927,1255.0620000000001 87 | 85,B0006_11,86,2008-05-07 12:01:49,24,3.531199617131715,-0.003266350518668096,34.22991790059867,0.0,0.0006,3058.157,1.45192377,1236.656 88 | 86,B0006_11,87,2008-05-07 16:59:29,24,3.540682828822266,-0.0025904387374867787,34.45467967969768,0.0,0.0008,3058.812,1.44714847,1236.844 89 | 87,B0006_11,88,2008-05-07 21:56:09,24,3.5356541187739614,-0.005834401131425425,34.803102905585625,0.0,0.0006,3058.578,1.44685381,1236.625 90 | 88,B0006_11,89,2008-05-08 02:53:49,24,3.5426480867538017,-0.0008107827825872327,34.81930482334886,0.0,0.0006,3049.328,1.44167419,1226.9060000000002 91 | 89,B0006_11,90,2008-05-09 12:25:07,24,3.5029468518556204,-0.004082848523023056,36.552436779815885,0.0,0.0006,3177.219,1.59358666,1395.875 92 | 90,B0006_11,91,2008-05-09 20:28:09,24,3.459144575143091,-0.00124371340722759,36.57029301599151,0.0,0.0006,3082.937,1.54639024,1432.8899999999999 93 | 91,B0006_11,92,2008-05-10 01:21:56,24,3.4885908514844224,0.00025707459183652856,35.95639890638621,0.0,0.0006,3074.2659999999996,1.51470559,1376.922 94 | 92,B0006_11,93,2008-05-10 06:18:12,24,3.5006625708235912,-0.0044565194706676844,35.572035216021405,0.0,0.0006,3066.0,1.49897861,1330.125 95 | 93,B0006_11,94,2008-05-10 11:16:04,24,3.519098513063688,-0.0005216459352215929,35.12626059216553,0.0,0.0006,3056.437,1.48311107,1302.234 96 | 94,B0006_11,95,2008-05-10 16:11:56,24,3.5212860952811007,0.002124212914898989,35.05245406960759,0.0,0.0006,3048.219,1.47296648,1273.9540000000002 97 | 95,B0006_11,96,2008-05-10 21:07:54,24,3.5315851155806786,-0.001277149325655288,34.99131293908511,0.0,0.0008,3039.218,1.46276044,1255.469 98 | 96,B0006_11,97,2008-05-11 02:05:21,24,3.532926287846672,-0.0028830904415017193,34.943599685891925,0.0,0.0008,3039.047,1.45183647,1236.453 99 | 97,B0006_11,98,2008-05-11 06:55:27,24,3.539045488750217,-0.004743717288306032,34.849835601441306,0.0,0.0006,3030.375,1.44686706,1227.4379999999999 100 | 98,B0006_11,99,2008-05-11 11:52:47,24,3.545547133777292,-0.005003296611138284,34.86099755076984,0.0,0.0008,3021.0,1.4413804,1207.937 101 | 99,B0006_11,100,2008-05-11 16:49:58,24,3.5484565862450688,-0.004076598785281695,34.837662550361905,0.0,0.0008,3021.75,1.43121074,1199.078 102 | 100,B0006_11,101,2008-05-11 21:47:12,24,3.549925959367978,-0.003369633278137025,34.87870780639173,0.0,0.0006,3012.265,1.42602472,1180.4679999999998 103 | 101,B0006_11,102,2008-05-12 02:36:58,24,3.5622369118183483,-0.003086654235800159,34.66799385095821,0.0,0.0006,3002.6409999999996,1.42048765,1171.1399999999999 104 | 102,B0006_11,103,2008-05-12 12:56:34,24,3.558769894565424,-0.0034861971592438762,35.2557669851797,0.0,0.0006,3012.0,1.42070062,1180.375 105 | 103,B0006_11,104,2008-05-12 18:59:59,24,3.5413834963307256,-0.0016814999715928005,35.871264189838314,0.0,0.0006,3020.11,1.4522558,1236.984 106 | 104,B0006_11,105,2008-05-12 23:53:17,24,3.5573203480565936,-0.003624230046266575,35.5586741930245,0.0,0.0006,3002.172000000006,1.43155763,1199.1090000000033 107 | 105,B0006_11,106,2008-05-13 04:35:25,24,3.5560021159887576,-0.0001220877718859728,35.37917332729928,0.0,0.0006,2992.703,1.41501575,1170.594 108 | 106,B0006_11,107,2008-05-13 09:24:04,24,3.5654509272008412,-0.003002244139122215,35.25835879194265,0.0,0.0008,2964.468,1.40973115,1142.797 109 | 107,B0006_11,108,2008-05-13 14:20:24,24,3.5778109276715733,-0.002660121308516546,34.977743574858515,0.0,0.0008,2983.375,1.40491732,1142.203 110 | 108,B0006_11,109,2008-05-13 19:04:20,24,3.5823842742694407,-0.004289379361839791,35.09536674698793,0.0,0.0006,2975.297,1.3951643,1133.859 111 | 109,B0006_11,110,2008-05-13 23:59:26,24,3.5836702091707866,-0.0025169601571893127,35.23466125554352,0.0,0.0008,2974.985000000001,1.39453438,1124.4069999999917 112 | 110,B0006_11,111,2008-05-14 04:55:57,24,3.5856253738844623,-0.0006328574526033285,35.0345809347327,0.0,0.0006,2966.25,1.38931712,1105.4379999999999 113 | 111,B0006_11,112,2008-05-14 09:52:22,24,3.5889842847237907,-0.0046533569917268525,34.959736236714605,0.0,0.0008,2956.3590000000004,1.38383795,1095.6879999999999 114 | 112,B0006_11,113,2008-05-14 14:35:22,24,3.5864700210588274,-0.004734773626631689,35.130364090229946,0.0,0.0008,2956.344,1.37368142,1086.359 115 | 113,B0006_11,114,2008-05-14 19:31:39,24,3.5923515752514272,-0.004164311052462543,35.31000839824853,0.0,0.0008,2956.406,1.37860599,1095.969 116 | 114,B0006_11,115,2008-05-15 00:27:55,24,3.5963124070242887,-0.00197131580435763,35.16961820965507,0.0,0.0006,2947.2659999999996,1.37376003,1077.531 117 | 115,B0006_11,116,2008-05-15 05:24:04,24,3.6002389127425705,-0.0032129390964392088,34.99384427968127,0.0,0.0008,2937.9840000000004,1.36825806,1058.625 118 | 116,B0006_11,117,2008-05-15 10:20:03,24,3.6042619870615136,-0.005286186125652595,34.87051338845891,0.0,0.0006,2929.125,1.3633024,1049.4379999999999 119 | 117,B0006_11,118,2008-05-15 15:10:59,24,3.6102087847682087,-0.00588001853136696,34.99150315814171,0.0,0.0008,2928.454,1.35809661,1058.625 120 | 118,B0006_11,119,2008-05-15 19:56:47,24,3.6043848473456417,-0.002697481794155657,35.370353548691504,0.0,0.0006,2929.375,1.35326909,1049.4689999999998 121 | 119,B0006_11,120,2008-05-16 16:54:59,24,3.5929395911706528,-0.0002897043696836975,35.35792668120537,0.0,0.0008,2946.031,1.38416057,1114.984 122 | 120,B0006_11,121,2008-05-16 21:37:39,24,3.5862615333641465,-0.002696693173890691,35.8874924214886,0.0,0.0006,2955.438,1.40514678,1133.422 123 | 121,B0006_11,122,2008-05-17 02:32:13,24,3.5993733218450874,-0.004809369279357301,35.41362624524575,0.0,0.0006,2937.625,1.37871902,1086.61 124 | 122,B0006_11,123,2008-05-17 07:25:19,24,3.613313370916122,-0.003695633181437169,34.86662650314206,0.0,0.0006,2929.078,1.36826578,1058.922 125 | 123,B0006_11,124,2008-05-17 12:21:15,24,3.612916400780757,0.002448164539924058,34.900465534838,-0.0,0.0006,2918.375,1.35731513,1039.547 126 | 124,B0006_11,125,2008-05-17 17:15:37,24,3.6230767623291897,-0.004482877775099519,34.624064867045256,0.0,0.0006,2919.812,1.35249346,1030.734 127 | 125,B0006_11,126,2008-05-17 22:10:51,24,3.6225410007004046,-0.00037257151055270735,34.74761764675118,0.0,0.0006,2900.422,1.34740992,1021.5000000000001 128 | 126,B0006_11,127,2008-05-18 03:05:19,24,3.6262759000790488,-0.0006780455442284406,34.606986338741144,0.0,0.0008,2901.375,1.33700751,1011.812 129 | 127,B0006_11,128,2008-05-18 08:00:50,24,3.6309395824903143,-0.0025625529319735227,34.3758393274177,0.0,0.0006,2901.187,1.3312353000000001,992.7189999999999 130 | 128,B0006_11,129,2008-05-18 12:44:48,24,3.63665215167077,-0.00375202512757255,34.21509258181516,0.0,0.0006,2891.64,1.32593038,983.2810000000001 131 | 129,B0006_11,130,2008-05-18 17:36:29,24,3.6336486748891437,0.0009710625802280873,34.40452574829636,0.0,0.0006,2882.906,1.32157874,983.875 132 | 130,B0006_11,131,2008-05-18 22:31:45,24,3.6408526421786505,-0.003630999262341847,34.27105538633811,0.0,0.0006,2883.265,1.32122507,974.4540000000001 133 | 131,B0006_11,132,2008-05-19 03:27:07,24,3.6463041446743727,-0.0016105886811120505,34.099498695535075,0.0,0.0008,2882.969,1.31564853,964.7660000000001 134 | 132,B0006_11,133,2008-05-19 15:34:33,24,3.643185525285694,-0.0051612468139402064,34.347474812989034,0.0,0.0006,2891.656,1.32090257,983.61 135 | 133,B0006_11,134,2008-05-19 20:59:48,24,3.63009179178362,-0.006681833318200413,34.64277824450123,0.0,0.0006,2901.25,1.34743288,1021.172 136 | 134,B0006_11,135,2008-05-20 01:16:42,24,3.6443831453068687,-0.003068345142732847,34.12594282508364,0.0,0.0006,2891.703,1.32601281,992.359 137 | 135,B0006_11,136,2008-05-20 06:12:06,24,3.6477248430882114,-0.00468703245478606,33.88737678207701,0.0,0.0006,2883.0159999999996,1.31011868,964.9850000000001 138 | 136,B0006_11,137,2008-05-20 11:07:23,24,3.6547588812996183,-0.0051591079110662715,33.56218507973103,0.0,0.0006,2873.453,1.3052162,955.297 139 | 137,B0006_11,138,2008-05-20 15:57:27,24,3.6498482167009336,-0.003632256560638578,33.97512946957805,-0.0,0.0006,2874.031,1.3004062,955.735 140 | 138,B0006_11,139,2008-05-20 20:50:39,24,3.654795053390779,-0.003771132033179804,33.9938311075272,0.0,0.0006,2874.2340000000004,1.30023574,955.594 141 | 139,B0006_11,140,2008-05-21 01:39:38,24,3.65519692144285,-0.0016085927490509671,33.80021082576708,0.0,0.0006,2864.9840000000004,1.28950667,937.0 142 | 140,B0006_11,141,2008-05-21 06:24:27,24,3.6609201852518987,-0.0036793388248434226,33.39907834598599,0.0,0.0006,2855.093,1.27925306,908.7339999999999 143 | 141,B0006_11,142,2008-05-21 11:18:06,24,3.6665506464743816,-0.003249394264921144,33.17152279184158,0.0,0.0006,2855.625,1.27920782,899.1410000000001 144 | 142,B0006_11,143,2008-05-21 16:10:21,24,3.664830420095059,-0.0012904707694538634,33.525893004486754,0.0,0.0006,2855.594,1.27403676,908.281 145 | 143,B0006_11,144,2008-05-21 21:02:09,24,3.6670708418070905,-0.002417411211403288,33.48434759874664,0.0,0.0006,2847.437,1.27412739,899.453 146 | 144,B0006_11,145,2008-05-22 01:42:59,24,3.6710638965547715,-0.0019144845855416184,33.05127195424509,0.0,0.0006,2847.36,1.26376394,890.015 147 | 145,B0006_11,146,2008-05-22 06:32:40,24,3.6755279905489777,-0.000216240567834002,32.79536153426087,0.0,0.0006,2837.328,1.25813737,871.078 148 | 146,B0006_11,147,2008-05-22 11:23:22,24,3.67291170661734,-0.00489984712722693,32.87846636576991,0.0,0.0008,2837.281,1.25325986,861.953 149 | 147,B0006_11,148,2008-05-22 16:17:58,24,3.67294260827938,-0.0015042475247667813,33.214326059104735,0.0,0.0006,2838.515,1.25323133,871.094 150 | 148,B0006_11,149,2008-05-22 20:59:50,24,3.6764268719270934,0.0018528412705641156,33.01008606676501,0.0,0.0006,2828.328,1.2480875,862.0160000000001 151 | 149,B0006_11,150,2008-05-23 12:13:16,24,3.678748349217624,-0.003839721422871184,32.832669483139746,0.0,0.0006,2838.265,1.25343543,871.203 152 | 150,B0006_11,151,2008-05-23 20:14:29,24,3.6660243812714266,-0.00583601993316971,33.95920158428766,0.0,0.0006,2864.547,1.28980488,936.828 153 | 151,B0006_11,152,2008-05-24 01:09:21,24,3.673517843188949,-0.0077447981233232455,33.66715541097441,0.0,0.0006,2846.39,1.2635402,899.188 154 | 152,B0006_11,153,2008-05-24 06:00:20,24,3.679265325617699,-0.0030678547998177143,33.290476668621174,0.0,0.0006,2837.969,1.24797617,871.25 155 | 153,B0006_11,154,2008-05-24 10:42:50,24,3.6810005302067896,-0.0010619899134000326,33.0677214501059,0.0,0.0006,2838.156,1.23212029,852.547 156 | 154,B0006_11,155,2008-05-24 15:31:10,24,3.6816210659707824,-0.005584674985657866,33.05886238959916,0.0,0.0006,2829.843,1.22711377,842.984 157 | 155,B0006_11,156,2008-05-24 20:21:13,24,3.6870580470143386,-0.0020995701539379295,32.806193089423736,0.0,0.0006,2829.625,1.22173976,834.172 158 | 156,B0006_11,157,2008-05-25 01:06:49,24,3.686609623032529,-0.0028067410403196155,32.85539211201931,0.0,0.0006,2819.437,1.21110268,824.266 159 | 157,B0006_11,158,2008-05-25 05:57:17,24,3.6886642285753455,-0.0032694873311616478,32.72492217663622,0.0,0.0006,2819.579,1.20561598,805.469 160 | 158,B0006_11,159,2008-05-25 10:51:37,24,3.689427929747468,-0.0038166754449602157,32.76928328797007,0.0,0.0006,2810.672,1.20089363,805.6869999999999 161 | 159,B0006_11,160,2008-05-25 15:37:08,24,3.691557712403205,-0.0020468242754553,32.59685985360409,0.0,0.0006,2811.485,1.1905903999999998,796.641 162 | 160,B0006_11,161,2008-05-25 20:23:04,24,3.6924946028305827,-0.002930439183863988,32.62058102581185,0.0,0.0006,2802.422,1.18517925,786.5310000000001 163 | 161,B0006_11,162,2008-05-26 01:13:02,24,3.693907128232746,-0.0013480330701835226,32.5455784721457,0.0,0.0006,2801.625,1.17967068,777.531 164 | 162,B0006_11,163,2008-05-26 06:01:08,24,3.6946262144743858,-0.0052291838766734605,32.43120345933949,0.0,0.0006,2802.031,1.17459528,768.203 165 | 163,B0006_11,164,2008-05-26 10:44:38,24,3.6963269652374176,-0.0007456920500808041,32.113473228514714,0.0,0.0006,2793.39,1.15381833,749.484 166 | 164,B0006_11,165,2008-05-26 15:30:43,24,3.6971696861624963,-0.001187802905123991,32.299362879069115,0.0,0.0006,2793.39,1.16440096,749.4379999999999 167 | 165,B0006_11,166,2008-05-26 20:21:04,24,3.6966744870459154,-0.0024571388608882225,32.416754062023976,0.0,0.0008,2792.485,1.15879749,749.125 168 | 166,B0006_11,167,2008-05-27 15:52:41,24,3.69305943579077,-0.0012447685738290848,32.371096995705706,0.0,0.0006,2802.0159999999996,1.17497488,805.625 169 | 167,B0006_11,168,2008-05-27 20:45:42,24,3.6918089829140865,-0.0031271288430358424,32.1923241277653,0.0,0.0006,2820.39,1.18567523,815.0 170 | -------------------------------------------------------------------------------- /LSTM_Paper/Window size5/log: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /LSTM_Paper/log: -------------------------------------------------------------------------------- 1 | 2 | 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https://raw.githubusercontent.com/Kalrfou/CapstoneProject/82409783f9fc9af84867fc690386c8d8f602dc94/Online/Cycle80-w5.png -------------------------------------------------------------------------------- /Online/log: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Online_Cap/B0006_on.csv: -------------------------------------------------------------------------------- 1 | ,Batt_name,cycle,dateTime,amb_temp,voltage_battery,current_battery,temp_battery,voltage_load,current_load,time,Capacity,NewCap 2 | 0,B0006_11,1,2008-04-02 15:25:41,24,2.4757677568281857,-2.0094358918725432,39.16298653238707,1.653,-1.999,3690.2340000000004,2.03533759,2.035895470614328 3 | 1,B0006_11,2,2008-04-02 19:43:48,24,2.35152551190002,-2.010374855522972,39.2462026826329,1.5430000000000001,-1.999,3672.344,2.02514025,2.0258526983299157 4 | 2,B0006_11,3,2008-04-03 00:01:06,24,2.440479715106544,-2.0085588871101128,38.99920246025285,1.65,-1.999,3651.6409999999996,2.01332637,2.0143192566260915 5 | 3,B0006_11,4,2008-04-03 04:16:37,24,2.479155853900392,-2.0092904478622464,38.84362777186652,1.69,-1.999,3631.563,2.01328467,2.0031266088037416 6 | 4,B0006_11,5,2008-04-03 08:33:25,24,2.280187848173442,-2.011760840220058,38.97798888258284,1.4880000000000002,-1.999,3629.172,2.00052834,2.0017898917112396 7 | 5,B0006_11,6,2008-04-03 12:55:10,24,2.4392842568466446,-2.0109470452247566,38.83989920742278,1.659,-1.999,3652.281,2.01389908,2.014702385503124 8 | 6,B0006_11,7,2008-04-03 17:17:16,24,2.2812068449406446,-2.012014626739587,39.04610837498471,1.505,-1.9992,3650.828,2.01310111,2.013890891433421 9 | 7,B0006_11,8,2008-04-03 21:28:14,24,2.3664105270296134,-2.009602654155728,38.87507521022778,1.6019999999999999,-1.9992,3572.453,1.96878983,1.970161717702064 10 | 8,B0006_11,9,2008-04-04 01:38:15,24,2.4360200495787083,-2.010503181695025,38.7260539826455,1.6640000000000001,-1.999,3550.594,1.96816618,1.9580389016482111 11 | 9,B0006_11,10,2008-04-04 05:48:08,24,2.1864377487643725,-2.0120006291945924,38.98629728904917,1.411,-1.999,3551.25,1.95723079,1.9584227890645254 12 | 10,B0006_11,11,2008-04-04 09:57:19,24,2.3406023605912454,-2.0086057639408743,38.659788619196426,1.5830000000000002,-1.9992,3530.25,1.94559915,1.9467240779930208 13 | 11,B0006_11,12,2008-04-04 17:56:27,24,2.4263021374140443,-2.008093198845442,38.84979195732924,1.659,-1.999,3491.0159999999996,1.9347505,1.9248432859297935 14 | 12,B0006_11,13,2008-04-04 22:01:54,24,2.475369770000704,-2.008861764489536,38.751283440151035,1.704,-1.999,3470.281,1.92327995,1.9132532409528349 15 | 13,B0006_11,14,2008-04-05 02:20:26,24,2.342364523655007,-2.0114258804383267,38.768537640587894,1.59,-1.999,3470.0,1.91188993,1.9131054362169801 16 | 14,B0006_11,15,2008-04-05 06:25:01,24,2.188400081882664,-2.0094401780170927,38.846861123443475,1.433,-1.999,3450.297,1.90106671,1.9021261949118322 17 | 15,B0006_11,16,2008-04-05 10:30:32,24,2.1206980945954057,-2.00629337660368,38.8658897816528,1.371,-1.999,3429.407,1.88919899,1.8904276978151264 18 | 16,B0006_11,17,2008-04-05 14:34:41,24,2.3699814577992986,-2.0083999032211324,38.647578733807656,1.625,-1.999,3409.4840000000004,1.87827837,1.87943379915425 19 | 17,B0006_11,18,2008-04-05 18:39:25,24,2.247991649924625,-2.0103074874707247,38.79218188173866,1.524,-1.999,3390.422,1.86756972,1.8687618561423847 20 | 18,B0006_11,19,2008-04-05 22:46:35,24,2.8844463698113665,-0.002277241069617877,38.770222239295286,0.0,-0.0008,3390.281,1.86758926,1.857387822098819 21 | 19,B0006_11,20,2008-04-18 21:10:19,24,2.203355846859377,-2.0099114516038865,37.54320599748628,1.445,-1.9992,3591.7340000000004,1.97962655,1.9809992279379753 22 | 20,B0006_11,21,2008-04-19 02:29:09,24,2.35887751155593,-2.010023846843136,37.90362577530197,1.619,-1.999,3552.297,1.95754991,1.9589114033043986 23 | 21,B0006_11,22,2008-04-19 07:47:15,24,2.218672914041916,-2.010621300102956,37.670160513988705,1.4909999999999999,-1.999,3531.578,1.94581502,1.9474446363006612 24 | 22,B0006_11,23,2008-04-19 13:01:42,24,2.339723057947995,-2.009836812236101,37.34166959188387,1.607,-1.999,3492.907,1.92424611,1.9257911247003683 25 | 23,B0006_11,24,2008-04-19 18:27:29,24,2.399750820894273,-2.008549110682199,37.28979017254232,1.653,-1.999,3511.594,1.94598113,1.9362854646927268 26 | 24,B0006_11,25,2008-04-19 23:41:55,24,2.491410879472442,-2.011253162152561,37.16794481484507,1.723,-1.999,3431.937000000005,1.90140793,1.8918219531613296 27 | 25,B0006_11,26,2008-04-20 04:54:41,24,2.7669269890921613,-0.0013483637694533895,37.51577848501052,0.0,-0.0008,3431.125,1.87867421,1.8802116532152127 28 | 26,B0006_11,27,2008-04-20 10:16:28,24,2.8356128467177317,-0.0009996515493716173,37.24165964092258,0.0,-0.0006,3412.406,1.86830613,1.869729341226429 29 | 27,B0006_11,28,2008-04-20 15:37:05,24,2.928394635151109,-0.003474005762546325,37.10097467364957,0.0,-0.0008,3410.688,1.85602441,1.8575716851690416 30 | 28,B0006_11,29,2008-04-20 20:50:38,24,3.0771149575987566,-0.0065849518216069824,36.53635083668188,0.0,-0.0006,3410.718,1.8451998,1.8352465058344771 31 | 29,B0006_11,30,2008-04-21 02:15:02,24,2.819560586845831,0.0017843402447845864,37.496077698962544,0.0,-0.0008,3392.719,1.85722889,1.8587050116485666 32 | 30,B0006_11,31,2008-04-22 15:33:49,24,2.434300686181212,-2.0101843550935627,37.250073961181094,1.607,1.999,3470.672,1.92477609,1.9192049964606372 33 | 31,B0006_11,32,2008-04-22 20:26:18,24,2.9187757391682454,0.00021994616902196602,37.27669935857522,0.0,0.0006,3414.1090000000004,1.88223175,1.8767921839644555 34 | 32,B0006_11,33,2008-04-23 01:18:11,24,2.973956908852585,-0.003806456592501386,37.053468915649404,0.0,0.0006,3395.219,1.85527669,1.8553304034594968 35 | 33,B0006_11,34,2008-04-23 06:18:19,24,3.0334333724088656,0.00115949398581303,36.49019770341733,0.0,0.0006,3386.0,1.83925089,1.8394879770836674 36 | 34,B0006_11,35,2008-04-23 11:08:00,24,3.0616925140357725,-0.0024706162055099685,36.17351619989374,0.0,0.0006,3368.187,1.81853961,1.8188007253744167 37 | 35,B0006_11,36,2008-04-23 16:06:25,24,3.0864214480405137,-0.002485656828321589,36.691721740616366,0.0,0.0006,3368.2659999999996,1.81347701,1.8134102136381813 38 | 36,B0006_11,37,2008-04-23 21:02:58,24,3.1377873218245425,-0.002908291707230441,36.30505451544784,0.0,0.0006,3358.719,1.79710185,1.7972839005098686 39 | 37,B0006_11,38,2008-04-24 01:51:33,24,3.1501156392374963,-0.0018286245424696968,36.06439554288408,0.0,0.0008,3339.5,1.78100463,1.7812711307318179 40 | 38,B0006_11,39,2008-04-24 06:50:00,24,3.1738920422499453,-0.008825733551338297,36.01731400053337,0.0,0.0006,3331.265,1.77101304,1.771286147560387 41 | 39,B0006_11,40,2008-04-24 11:49:24,24,3.1972071497430035,-0.003438126514516957,35.90775442895192,0.0,0.0006,3321.188,1.76047124,1.760343651689457 42 | 40,B0006_11,41,2008-04-24 16:45:56,24,3.227568458837217,-0.00492719060429448,36.35807936248104,0.0,0.0006,3321.844,1.75029138,1.7500083653756893 43 | 41,B0006_11,42,2008-04-24 21:38:11,24,3.2308580821504327,-0.001453011628706404,36.50421625915163,0.0,0.0006,3311.828,1.7394924999999999,1.7391965454363252 44 | 42,B0006_11,43,2008-04-25 12:03:44,24,3.199633267433362,-0.0022925624123046186,36.991264831075426,0.0,-0.0008,3327.3909999999996,1.75467666,1.7558399379239968 45 | 43,B0006_11,44,2008-04-25 20:03:55,24,3.1898103242112006,-0.002874522115121392,36.81410785577819,-0.0,0.0006,3312.078,1.76593909,1.7660766424451395 46 | 44,B0006_11,45,2008-04-26 01:04:35,24,3.2499622126804124,-0.004430988718560468,35.94866550135389,0.0,0.0006,3293.172,1.73378874,1.7340508916049076 47 | 45,B0006_11,46,2008-04-26 05:57:35,24,3.276178621777498,-0.0028865069759319247,35.80761414587771,0.0,0.0006,3275.328,1.71332591,1.71337316064678 48 | 46,B0006_11,47,2008-04-26 10:58:25,24,3.2892337937863383,-0.001482135856283325,35.727411052391915,0.0,0.0008,3265.0159999999996,1.70240778,1.7024206580601362 49 | 47,B0006_11,48,2008-04-29 12:15:56,24,2.934951679828825,-0.0026359580742833216,39.57378670033301,0.0,0.0008,3328.937,1.82371946,1.8238566593668397 50 | 48,B0006_11,49,2008-04-29 17:05:27,24,3.0133583509832325,-0.0025141196723349147,38.781959990934574,-0.0,0.0008,3329.2659999999996,1.80812841,1.808049329137483 51 | 49,B0006_11,50,2008-04-29 22:00:04,24,3.0981920097302105,0.0006501043390256764,37.8590470128016,0.0,0.0006,3301.579,1.77576109,1.7763684993649742 52 | 50,B0006_11,51,2008-04-30 02:43:55,24,3.167346834112308,0.0011884652184539464,37.12135762318553,0.0,0.0006,3283.563,1.74474098,1.7449666635858003 53 | 51,B0006_11,52,2008-04-30 07:41:42,24,3.2286855117160864,-0.0035102794129954827,36.580486706224,0.0,0.0006,3275.125,1.72920703,1.729357285150454 54 | 52,B0006_11,53,2008-04-30 12:28:49,24,3.2595724548548413,-0.0012542411110933905,36.436407503512335,0.0,0.0006,3265.656,1.70810866,1.7081770382122006 55 | 53,B0006_11,54,2008-04-30 17:20:16,24,3.2774199521442657,-0.0024642794163139943,36.53091042103418,0.0,0.0006,3266.093,1.69759542,1.6974651095182622 56 | 54,B0006_11,55,2008-04-30 22:14:54,24,3.309541910489555,-0.0037357037492950548,36.06449507955633,0.0,0.0006,3257.594,1.68206345,1.6820892041795512 57 | 55,B0006_11,56,2008-05-01 03:15:33,24,3.3229457962953464,-0.004205019176247785,35.94327250944611,0.0,0.0008,3248.593,1.67147683,1.6714682789786905 58 | 56,B0006_11,57,2008-05-01 08:16:03,24,3.3268774953647884,-0.0045774706794028865,35.89295328706184,0.0,0.0006,3238.7659999999996,1.66075914,1.6607462393440797 59 | 57,B0006_11,58,2008-05-01 13:14:24,24,3.336400100467817,-0.004220549753628036,35.959515024229205,0.0,0.0008,3240.14,1.65112102,1.6507832080859774 60 | 58,B0006_11,59,2008-05-01 18:11:10,24,3.3562313241233728,-0.004630300153985874,36.03733181811758,0.0,0.0008,3230.1090000000004,1.64013493,1.6399611339016154 61 | 59,B0006_11,60,2008-05-01 23:10:15,24,3.371466361383496,0.0014743883145177995,35.71327174023829,0.0,0.0006,3229.781000000003,1.62919994,1.6291552545343395 62 | 60,B0006_11,61,2008-05-02 03:53:34,24,3.3960187616989215,-0.004672744022970511,35.366498347395265,0.0,0.0006,3212.469,1.60884963,1.6087738228070116 63 | 61,B0006_11,62,2008-05-02 08:44:40,24,3.393882521860081,-0.004211278055821824,35.18665272384657,0.0,0.0006,3213.219,1.60352449,1.6034556801663566 64 | 62,B0006_11,63,2008-05-02 13:43:05,24,3.3978158698372107,-0.00442104201360729,35.67614711684021,-0.0,0.0008,3203.578,1.59897121,1.5982629273032622 65 | 63,B0006_11,64,2008-05-02 19:07:48,24,3.416423032732188,-0.0003200027950143793,34.71660828900691,0.0,0.0008,3195.125,1.58762731,1.5878553902170989 66 | 64,B0006_11,65,2008-05-02 23:24:17,24,3.417549528683359,-0.001444462241441726,35.028736379559405,0.0,0.0006,3195.3129999999946,1.58297197,1.582866078540884 67 | 65,B0006_11,66,2008-05-03 04:13:08,24,3.4275053861483777,-0.0034826648553054514,34.774991305740066,0.0,0.0006,3186.297,1.56713764,1.5671388827099244 68 | 66,B0006_11,67,2008-05-03 09:10:19,24,3.4347508426334845,-0.0044826484110488144,34.69789797592927,0.0,0.0006,3176.438,1.56134461,1.5613863246182287 69 | 67,B0006_11,68,2008-05-03 14:04:34,24,3.4481980199249085,-0.0012195271207717936,34.6005415728579,0.0,0.0006,3167.593,1.55117119,1.5511699027073607 70 | 68,B0006_11,69,2008-05-03 18:58:13,24,3.4471363584937484,0.0007226343450961433,34.655186314766375,0.0,0.0008,3167.719,1.54067416,1.5458079524572257 71 | 69,B0006_11,70,2008-05-03 23:42:30,24,3.4551154405959217,-0.0019271431845461406,34.49661503681511,0.0,0.0006,3159.077999999994,1.52531387,1.5303512201282203 72 | 70,B0006_11,71,2008-05-04 04:41:40,24,3.468023005179232,-0.0012882276547055275,34.58789396630877,0.0,0.0006,3148.829,1.53015703,1.530037068779896 73 | 71,B0006_11,72,2008-05-04 09:40:11,24,3.4711335972831856,-0.00449031192608329,34.48391218889425,0.0,0.0006,3149.094,1.52465668,1.524502879440856 74 | 72,B0006_11,73,2008-05-04 14:38:13,24,3.4883390886919767,-0.001221569208727667,34.32316829732572,0.0,0.0008,3140.547,1.5145493,1.5143421217576885 75 | 73,B0006_11,74,2008-05-04 19:37:07,24,3.4882264404681207,-9.1970080813463e-05,34.371124137441896,0.0,0.0006,3131.625,1.5093915,1.5093347290472643 76 | 74,B0006_11,75,2008-05-05 00:35:53,24,3.4877002346746275,-0.0025462031829215142,34.37262003693859,0.0,0.0006,3121.907,1.50399743,1.5038629573593818 77 | 75,B0006_11,76,2008-05-05 05:28:13,24,3.5019144212894115,-0.0018050512867516227,34.06737352776938,0.0,0.0006,3122.125,1.49352019,1.4934141968459886 78 | 76,B0006_11,77,2008-05-05 11:41:31,24,3.5064898836985074,-0.002346995765496664,34.01427816460032,0.0,0.0006,3121.672,1.4876323,1.492961555103451 79 | 77,B0006_11,78,2008-05-05 20:40:08,24,3.4612712556588887,-0.0015893640732856102,35.633900290416996,0.0,0.0008,3120.86,1.53028762,1.5353905505304548 80 | 78,B0006_11,79,2008-05-06 01:38:46,24,3.49597974716647,-0.002813062156713118,34.63193513275843,0.0,0.0006,3103.828,1.50407601,1.5040992507032167 81 | 79,B0006_11,80,2008-05-06 06:37:06,24,3.4958682327661417,-0.002245547841319663,34.48366479877386,0.0,0.0006,3095.094,1.48875936,1.4939343208221056 82 | 80,B0006_11,81,2008-05-06 11:25:21,24,3.5168324777654205,-0.003048177774026196,34.198906360730284,0.0,0.0006,3095.781,1.47827793,1.4780664037394273 83 | 81,B0006_11,82,2008-05-06 16:23:36,24,3.5140805590225344,-0.003030933482684179,34.73440888809496,0.0,0.0006,3094.328,1.47845998,1.4777782215645938 84 | 82,B0006_11,83,2008-05-06 21:21:49,24,3.5179169265872945,-0.0031973699811009533,34.854516068350875,0.0,0.0006,3086.531,1.47321484,1.472936463003593 85 | 83,B0006_11,84,2008-05-07 02:17:25,24,3.5191016758451497,-0.002832656974994741,34.71454754205853,0.0,0.0006,3085.625,1.46751649,1.4671741813566317 86 | 84,B0006_11,85,2008-05-07 07:04:02,24,3.521493643197122,-0.0023048413012049227,34.408441964409874,0.0,0.0006,3067.203,1.45162927,1.4566361807199766 87 | 85,B0006_11,86,2008-05-07 12:01:49,24,3.531199617131715,-0.003266350518668096,34.22991790059867,0.0,0.0006,3058.157,1.45192377,1.4517793914557977 88 | 86,B0006_11,87,2008-05-07 16:59:29,24,3.540682828822266,-0.0025904387374867787,34.45467967969768,0.0,0.0008,3058.812,1.44714847,1.4466701203826209 89 | 87,B0006_11,88,2008-05-07 21:56:09,24,3.5356541187739614,-0.005834401131425425,34.803102905585625,0.0,0.0006,3058.578,1.44685381,1.4464780474868046 90 | 88,B0006_11,89,2008-05-08 02:53:49,24,3.5426480867538017,-0.0008107827825872327,34.81930482334886,0.0,0.0006,3049.328,1.44167419,1.4413195156069252 91 | 89,B0006_11,90,2008-05-09 12:25:07,24,3.5029468518556204,-0.004082848523023056,36.552436779815885,0.0,0.0006,3177.219,1.59358666,1.5932108797593085 92 | 90,B0006_11,91,2008-05-09 20:28:09,24,3.459144575143091,-0.00124371340722759,36.57029301599151,0.0,0.0006,3082.937,1.54639024,1.545816931494205 93 | 91,B0006_11,92,2008-05-10 01:21:56,24,3.4885908514844224,0.00025707459183652856,35.95639890638621,0.0,0.0006,3074.2659999999996,1.51470559,1.5143975642530008 94 | 92,B0006_11,93,2008-05-10 06:18:12,24,3.5006625708235912,-0.0044565194706676844,35.572035216021405,0.0,0.0006,3066.0,1.49897861,1.4987297354903961 95 | 93,B0006_11,94,2008-05-10 11:16:04,24,3.519098513063688,-0.0005216459352215929,35.12626059216553,0.0,0.0006,3056.437,1.48311107,1.4828003485967876 96 | 94,B0006_11,95,2008-05-10 16:11:56,24,3.5212860952811007,0.002124212914898989,35.05245406960759,0.0,0.0006,3048.219,1.47296648,1.4725710237938636 97 | 95,B0006_11,96,2008-05-10 21:07:54,24,3.5315851155806786,-0.001277149325655288,34.99131293908511,0.0,0.0008,3039.218,1.46276044,1.4622728957173121 98 | 96,B0006_11,97,2008-05-11 02:05:21,24,3.532926287846672,-0.0028830904415017193,34.943599685891925,0.0,0.0008,3039.047,1.45183647,1.4566554816846509 99 | 97,B0006_11,98,2008-05-11 06:55:27,24,3.539045488750217,-0.004743717288306032,34.849835601441306,0.0,0.0006,3030.375,1.44686706,1.4465451153462376 100 | 98,B0006_11,99,2008-05-11 11:52:47,24,3.545547133777292,-0.005003296611138284,34.86099755076984,0.0,0.0008,3021.0,1.4413804,1.4410326606114734 101 | 99,B0006_11,100,2008-05-11 16:49:58,24,3.5484565862450688,-0.004076598785281695,34.837662550361905,0.0,0.0008,3021.75,1.43121074,1.4361455332134425 102 | 100,B0006_11,101,2008-05-11 21:47:12,24,3.549925959367978,-0.003369633278137025,34.87870780639173,0.0,0.0006,3012.265,1.42602472,1.4310046417640125 103 | 101,B0006_11,102,2008-05-12 02:36:58,24,3.5622369118183483,-0.003086654235800159,34.66799385095821,0.0,0.0006,3002.6409999999996,1.42048765,1.4201936035989466 104 | 102,B0006_11,103,2008-05-12 12:56:34,24,3.558769894565424,-0.0034861971592438762,35.2557669851797,0.0,0.0006,3012.0,1.42070062,1.4254122739149302 105 | 103,B0006_11,104,2008-05-12 18:59:59,24,3.5413834963307256,-0.0016814999715928005,35.871264189838314,0.0,0.0006,3020.11,1.4522558,1.4516088089491692 106 | 104,B0006_11,105,2008-05-12 23:53:17,24,3.5573203480565936,-0.003624230046266575,35.5586741930245,0.0,0.0006,3002.172000000006,1.43155763,1.43098795738246 107 | 105,B0006_11,106,2008-05-13 04:35:25,24,3.5560021159887576,-0.0001220877718859728,35.37917332729928,0.0,0.0006,2992.703,1.41501575,1.419868920904065 108 | 106,B0006_11,107,2008-05-13 09:24:04,24,3.5654509272008412,-0.003002244139122215,35.25835879194265,0.0,0.0008,2964.468,1.40973115,1.409553587878249 109 | 107,B0006_11,108,2008-05-13 14:20:24,24,3.5778109276715733,-0.002660121308516546,34.977743574858515,0.0,0.0008,2983.375,1.40491732,1.404086339312217 110 | 108,B0006_11,109,2008-05-13 19:04:20,24,3.5823842742694407,-0.004289379361839791,35.09536674698793,0.0,0.0006,2975.297,1.3951643,1.394480802353087 111 | 109,B0006_11,110,2008-05-13 23:59:26,24,3.5836702091707866,-0.0025169601571893127,35.23466125554352,0.0,0.0008,2974.985000000001,1.39453438,1.3941490611345464 112 | 110,B0006_11,111,2008-05-14 04:55:57,24,3.5856253738844623,-0.0006328574526033285,35.0345809347327,0.0,0.0006,2966.25,1.38931712,1.3889805947677574 113 | 111,B0006_11,112,2008-05-14 09:52:22,24,3.5889842847237907,-0.0046533569917268525,34.959736236714605,0.0,0.0008,2956.3590000000004,1.38383795,1.383338071867178 114 | 112,B0006_11,113,2008-05-14 14:35:22,24,3.5864700210588274,-0.004734773626631689,35.130364090229946,0.0,0.0008,2956.344,1.37368142,1.3781180044122 115 | 113,B0006_11,114,2008-05-14 19:31:39,24,3.5923515752514272,-0.004164311052462543,35.31000839824853,0.0,0.0008,2956.406,1.37860599,1.3781263820756213 116 | 114,B0006_11,115,2008-05-15 00:27:55,24,3.5963124070242887,-0.00197131580435763,35.16961820965507,0.0,0.0006,2947.2659999999996,1.37376003,1.3733489933036573 117 | 115,B0006_11,116,2008-05-15 05:24:04,24,3.6002389127425705,-0.0032129390964392088,34.99384427968127,0.0,0.0008,2937.9840000000004,1.36825806,1.3678505415176445 118 | 116,B0006_11,117,2008-05-15 10:20:03,24,3.6042619870615136,-0.005286186125652595,34.87051338845891,0.0,0.0006,2929.125,1.3633024,1.3626891819757776 119 | 117,B0006_11,118,2008-05-15 15:10:59,24,3.6102087847682087,-0.00588001853136696,34.99150315814171,0.0,0.0008,2928.454,1.35809661,1.3573698801881666 120 | 118,B0006_11,119,2008-05-15 19:56:47,24,3.6043848473456417,-0.002697481794155657,35.370353548691504,0.0,0.0006,2929.375,1.35326909,1.357631189719719 121 | 119,B0006_11,120,2008-05-16 16:54:59,24,3.5929395911706528,-0.0002897043696836975,35.35792668120537,0.0,0.0008,2946.031,1.38416057,1.3835754527527957 122 | 120,B0006_11,121,2008-05-16 21:37:39,24,3.5862615333641465,-0.002696693173890691,35.8874924214886,0.0,0.0006,2955.438,1.40514678,1.4044901004426096 123 | 121,B0006_11,122,2008-05-17 02:32:13,24,3.5993733218450874,-0.004809369279357301,35.41362624524575,0.0,0.0006,2937.625,1.37871902,1.3836406854446546 124 | 122,B0006_11,123,2008-05-17 07:25:19,24,3.613313370916122,-0.003695633181437169,34.86662650314206,0.0,0.0006,2929.078,1.36826578,1.367994570458544 125 | 123,B0006_11,124,2008-05-17 12:21:15,24,3.612916400780757,0.002448164539924058,34.900465534838,-0.0,0.0006,2918.375,1.35731513,1.3622333738066268 126 | 124,B0006_11,125,2008-05-17 17:15:37,24,3.6230767623291897,-0.004482877775099519,34.624064867045256,0.0,0.0006,2919.812,1.35249346,1.3523102997415877 127 | 125,B0006_11,126,2008-05-17 22:10:51,24,3.6225410007004046,-0.00037257151055270735,34.74761764675118,0.0,0.0006,2900.422,1.34740992,1.3470887792039545 128 | 126,B0006_11,127,2008-05-18 03:05:19,24,3.6262759000790488,-0.0006780455442284406,34.606986338741144,0.0,0.0008,2901.375,1.33700751,1.341973268954162 129 | 127,B0006_11,128,2008-05-18 08:00:50,24,3.6309395824903143,-0.0025625529319735227,34.3758393274177,0.0,0.0006,2901.187,1.3312353000000001,1.3362931254249404 130 | 128,B0006_11,129,2008-05-18 12:44:48,24,3.63665215167077,-0.00375202512757255,34.21509258181516,0.0,0.0006,2891.64,1.32593038,1.3257251437235589 131 | 129,B0006_11,130,2008-05-18 17:36:29,24,3.6336486748891437,0.0009710625802280873,34.40452574829636,0.0,0.0006,2882.906,1.32157874,1.326300725844322 132 | 130,B0006_11,131,2008-05-18 22:31:45,24,3.6408526421786505,-0.003630999262341847,34.27105538633811,0.0,0.0006,2883.265,1.32122507,1.3209270918817524 133 | 131,B0006_11,132,2008-05-19 03:27:07,24,3.6463041446743727,-0.0016105886811120505,34.099498695535075,0.0,0.0008,2882.969,1.31564853,1.315440331712007 134 | 132,B0006_11,133,2008-05-19 15:34:33,24,3.643185525285694,-0.0051612468139402064,34.347474812989034,0.0,0.0006,2891.656,1.32090257,1.3207204446910052 135 | 133,B0006_11,134,2008-05-19 20:59:48,24,3.63009179178362,-0.006681833318200413,34.64277824450123,0.0,0.0006,2901.25,1.34743288,1.3472615426468908 136 | 134,B0006_11,135,2008-05-20 01:16:42,24,3.6443831453068687,-0.003068345142732847,34.12594282508364,0.0,0.0006,2891.703,1.32601281,1.3258172017975836 137 | 135,B0006_11,136,2008-05-20 06:12:06,24,3.6477248430882114,-0.00468703245478606,33.88737678207701,0.0,0.0006,2883.0159999999996,1.31011868,1.3152865371158968 138 | 136,B0006_11,137,2008-05-20 11:07:23,24,3.6547588812996183,-0.0051591079110662715,33.56218507973103,0.0,0.0006,2873.453,1.3052162,1.3049417456732726 139 | 137,B0006_11,138,2008-05-20 15:57:27,24,3.6498482167009336,-0.003632256560638578,33.97512946957805,-0.0,0.0006,2874.031,1.3004062,1.3052118833342572 140 | 138,B0006_11,139,2008-05-20 20:50:39,24,3.654795053390779,-0.003771132033179804,33.9938311075272,0.0,0.0006,2874.2340000000004,1.30023574,1.300010818429479 141 | 139,B0006_11,140,2008-05-21 01:39:38,24,3.65519692144285,-0.0016085927490509671,33.80021082576708,0.0,0.0006,2864.9840000000004,1.28950667,1.2946099781519183 142 | 140,B0006_11,141,2008-05-21 06:24:27,24,3.6609201852518987,-0.0036793388248434226,33.39907834598599,0.0,0.0006,2855.093,1.27925306,1.2844944918239223 143 | 141,B0006_11,142,2008-05-21 11:18:06,24,3.6665506464743816,-0.003249394264921144,33.17152279184158,0.0,0.0006,2855.625,1.27920782,1.2788531369744722 144 | 142,B0006_11,143,2008-05-21 16:10:21,24,3.664830420095059,-0.0012904707694538634,33.525893004486754,0.0,0.0006,2855.594,1.27403676,1.278817944890673 145 | 143,B0006_11,144,2008-05-21 21:02:09,24,3.6670708418070905,-0.002417411211403288,33.48434759874664,0.0,0.0006,2847.437,1.27412739,1.2738651664921143 146 | 144,B0006_11,145,2008-05-22 01:42:59,24,3.6710638965547715,-0.0019144845855416184,33.05127195424509,0.0,0.0006,2847.36,1.26376394,1.2634957464884886 147 | 145,B0006_11,146,2008-05-22 06:32:40,24,3.6755279905489777,-0.000216240567834002,32.79536153426087,0.0,0.0006,2837.328,1.25813737,1.2579447345764132 148 | 146,B0006_11,147,2008-05-22 11:23:22,24,3.67291170661734,-0.00489984712722693,32.87846636576991,0.0,0.0008,2837.281,1.25325986,1.2582147626595885 149 | 147,B0006_11,148,2008-05-22 16:17:58,24,3.67294260827938,-0.0015042475247667813,33.214326059104735,0.0,0.0006,2838.515,1.25323133,1.2581014301181428 150 | 148,B0006_11,149,2008-05-22 20:59:50,24,3.6764268719270934,0.0018528412705641156,33.01008606676501,0.0,0.0006,2828.328,1.2480875,1.2476108170670914 151 | 149,B0006_11,150,2008-05-23 12:13:16,24,3.678748349217624,-0.003839721422871184,32.832669483139746,0.0,0.0006,2838.265,1.25343543,1.2530299292508327 152 | 150,B0006_11,151,2008-05-23 20:14:29,24,3.6660243812714266,-0.00583601993316971,33.95920158428766,0.0,0.0006,2864.547,1.28980488,1.2892147512025867 153 | 151,B0006_11,152,2008-05-24 01:09:21,24,3.673517843188949,-0.0077447981233232455,33.66715541097441,0.0,0.0006,2846.39,1.2635402,1.2631255809766277 154 | 152,B0006_11,153,2008-05-24 06:00:20,24,3.679265325617699,-0.0030678547998177143,33.290476668621174,0.0,0.0006,2837.969,1.24797617,1.2475626092931893 155 | 153,B0006_11,154,2008-05-24 10:42:50,24,3.6810005302067896,-0.0010619899134000326,33.0677214501059,0.0,0.0006,2838.156,1.23212029,1.2368429636731237 156 | 154,B0006_11,155,2008-05-24 15:31:10,24,3.6816210659707824,-0.005584674985657866,33.05886238959916,0.0,0.0006,2829.843,1.22711377,1.231891535593213 157 | 155,B0006_11,156,2008-05-24 20:21:13,24,3.6870580470143386,-0.0020995701539379295,32.806193089423736,0.0,0.0006,2829.625,1.22173976,1.2213406085477723 158 | 156,B0006_11,157,2008-05-25 01:06:49,24,3.686609623032529,-0.0028067410403196155,32.85539211201931,0.0,0.0006,2819.437,1.21110268,1.215979145852351 159 | 157,B0006_11,158,2008-05-25 05:57:17,24,3.6886642285753455,-0.0032694873311616478,32.72492217663622,0.0,0.0006,2819.579,1.20561598,1.2105871021532275 160 | 158,B0006_11,159,2008-05-25 10:51:37,24,3.689427929747468,-0.0038166754449602157,32.76928328797007,0.0,0.0006,2810.672,1.20089363,1.2057083773988064 161 | 159,B0006_11,160,2008-05-25 15:37:08,24,3.691557712403205,-0.0020468242754553,32.59685985360409,0.0,0.0006,2811.485,1.1905903999999998,1.1953626726597082 162 | 160,B0006_11,161,2008-05-25 20:23:04,24,3.6924946028305827,-0.002930439183863988,32.62058102581185,0.0,0.0006,2802.422,1.18517925,1.1900370254076678 163 | 161,B0006_11,162,2008-05-26 01:13:02,24,3.693907128232746,-0.0013480330701835226,32.5455784721457,0.0,0.0006,2801.625,1.17967068,1.1845630892012111 164 | 162,B0006_11,163,2008-05-26 06:01:08,24,3.6946262144743858,-0.0052291838766734605,32.43120345933949,0.0,0.0006,2802.031,1.17459528,1.1794316558369449 165 | 163,B0006_11,164,2008-05-26 10:44:38,24,3.6963269652374176,-0.0007456920500808041,32.113473228514714,0.0,0.0006,2793.39,1.15381833,1.1584618353982652 166 | 164,B0006_11,165,2008-05-26 15:30:43,24,3.6971696861624963,-0.001187802905123991,32.299362879069115,0.0,0.0006,2793.39,1.16440096,1.1638702150485654 167 | 165,B0006_11,166,2008-05-26 20:21:04,24,3.6966744870459154,-0.0024571388608882225,32.416754062023976,0.0,0.0008,2792.485,1.15879749,1.1634696466953351 168 | 166,B0006_11,167,2008-05-27 15:52:41,24,3.69305943579077,-0.0012447685738290848,32.371096995705706,0.0,0.0006,2802.0159999999996,1.17497488,1.1795905830044042 169 | 167,B0006_11,168,2008-05-27 20:45:42,24,3.6918089829140865,-0.0031271288430358424,32.1923241277653,0.0,0.0006,2820.39,1.18567523,1.1898919499037302 170 | -------------------------------------------------------------------------------- /Online_Cap/log: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /PythonCode/New_Cap_online.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Tue Apr 23 22:30:53 2019 5 | 6 | @author: khaled 7 | """ 8 | 9 | import pandas as pd 10 | import numpy as np 11 | #dataset_caharge=pd.read_csv("B0005_10_charge.csv") 12 | dataset_discharge=pd.read_csv("New_dataset_LSTM.csv") 13 | selec_battery_dataset=dataset_discharge[(dataset_discharge.Batt_name =="'B0005_14'")| 14 | (dataset_discharge.Batt_name =="'B0006_16'")|(dataset_discharge.Batt_name =="'B0007_13'") 15 | |(dataset_discharge.Batt_name =="'B0018_15'")] 16 | selec_battery_dataset.to_csv("Batt_56718.csv") 17 | ###################################################### 18 | selec_battery_dataset=pd.read_csv("Batt_56718.csv") 19 | 20 | #This function read each row form the dataset and finds the current battery when the voltage drop to vol 21 | #These Lists used for creating new dataframe 22 | l1,l2,l3,l4,l5,l6,l7=[],[],[],[],[],[],[] 23 | def get_list_C_T(dataset, vol): 24 | cycle=1 25 | check=True 26 | current=0 27 | time=0.0 28 | sum_discharge=[] 29 | time_dis=[] 30 | i=0 31 | names=dataset['Batt_name'][0] 32 | for j in range(len(dataset)): 33 | if names=="'B0005_14'": 34 | vol=2.7 35 | elif names=="'B0006_16'": 36 | vol=2.5 37 | elif names=="'B0007_13'": 38 | vol=2.2 39 | else: 40 | vol=2.5 41 | if cycle == dataset['cycle'][j] and names==dataset['Batt_name'][j]: 42 | if dataset['voltage_battery'][j]>= vol and check: 43 | current=dataset['current_battery'][j] 44 | time=dataset['time'][j] 45 | #i=i+1 46 | 47 | else: 48 | sum_discharge.append(current) 49 | time_dis.append(time) 50 | l1.append(dataset['cycle'][j-1]) 51 | l2.append(dataset['voltage_battery'][j-1]) 52 | l3.append(dataset['time'][j-1]) 53 | l4.append(dataset['temp_battery'][j-1]) 54 | l5.append(names) 55 | l6.append(dataset['dateTime'][j-1]) 56 | l7.append(dataset['amb_temp'][j-1]) 57 | i=i+1 58 | #check=False 59 | else: 60 | cycle=dataset['cycle'][j] 61 | names=dataset['Batt_name'][j] 62 | current=0 63 | if names != dataset['Batt_name'][j]: 64 | i=0 65 | return sum_discharge,time_dis 66 | ######################################################### 67 | sum_discharge=[] 68 | time_dis=[] 69 | sum_discharge,time_dis=get_list_C_T(selec_battery_dataset,2.7) 70 | print(len(sum_discharge)) 71 | 72 | ########################################################## 73 | new_dataset=pd.DataFrame() 74 | new_dataset['Batt_name']=l5 75 | new_dataset['cycle']=l1 76 | new_dataset['dateTime']=l6 77 | new_dataset['amb_temp']=l7 78 | new_dataset['voltage_battery']=l2 79 | new_dataset['temp_battery']=l4 80 | new_dataset['current_battery']=sum_discharge 81 | new_dataset['time']=l3 82 | #new_dataset['Capacity_online']=res 83 | ######################################################### 84 | initCap=[1.8910523,1.85648742,1.85500452,2.03533759] 85 | # This function do Calculation for Capcaity of all discharge cycle that store in the data 86 | def Cap_Cal(dataset): 87 | list_Cap=[] 88 | Cap=[] 89 | cap=0 90 | ini_cap=initCap[cap] 91 | Cap.append(ini_cap) 92 | j=0 93 | names=dataset['Batt_name'][0] 94 | for i in range(len(dataset)): 95 | if j==0 and names == dataset['Batt_name'][i]: 96 | list_Cap.append(ini_cap) 97 | print(ini_cap) 98 | j=j+1 99 | elif j!=0 and names == dataset['Batt_name'][i]: 100 | dt=(dataset['time'][i] -dataset['time'][i-1]) 101 | currents=(dataset['current_battery'][i])#+ sum_discharge[i-1])/2 102 | Cap_0=list_Cap[i-1] 103 | list_Cap.append((Cap_0-((currents*dt))/(3600))) 104 | elif j!=0 and names != dataset['Batt_name'][i] : 105 | print(i) 106 | names=dataset['Batt_name'][i] 107 | cap=cap+1 108 | ini_cap=initCap[cap] 109 | list_Cap.append(ini_cap) 110 | 111 | return list_Cap 112 | ########################################################################### 113 | 114 | res=Cap_Cal(new_dataset) 115 | 116 | new_dataset['Online_Capacity']=res 117 | new_dataset.to_csv("Coll_Cap_file1.csv") 118 | ############testing############################# 119 | test_data=new_dataset[(new_dataset.Batt_name=="'B0018_15'")] 120 | res=test_data['Online_Capacity'] 121 | -------------------------------------------------------------------------------- /PythonCode/STLM_RUL_Multi: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /PythonCode/STLM_multi.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Mon Apr 1 09:02:14 2019 5 | 6 | @author: khaled 7 | """ 8 | import sys 9 | import numpy as np # linear algebra 10 | from scipy.stats import randint 11 | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL 12 | import matplotlib.pyplot as plt # this is used for the plot the graph 13 | import seaborn as sns # used for plot interactive graph. 14 | from sklearn.model_selection import train_test_split # to split the data into two parts 15 | #from sklearn.cross_validation import KFold # use for cross validation 16 | from sklearn.preprocessing import StandardScaler # for normalization 17 | from sklearn.preprocessing import MinMaxScaler 18 | from sklearn.pipeline import Pipeline # pipeline making 19 | from sklearn.model_selection import cross_val_score 20 | from sklearn.feature_selection import SelectFromModel 21 | from sklearn import metrics # for the check the error and accuracy of the model 22 | from sklearn.metrics import mean_squared_error,r2_score 23 | 24 | ## for Deep-learing: 25 | import keras 26 | from keras.layers import Dense 27 | from keras.models import Sequential 28 | from keras.utils import to_categorical 29 | from keras.optimizers import SGD 30 | from keras.callbacks import EarlyStopping 31 | from keras.utils import np_utils 32 | import itertools 33 | from keras.layers import LSTM 34 | from keras.layers.convolutional import Conv1D 35 | from keras.layers.convolutional import MaxPooling1D 36 | from keras.layers import Dropout 37 | 38 | 39 | 40 | df=pd.read_csv("discharge_updateSoH.csv") 41 | 42 | featurs=['Batt_name','cycle','amb_temp','voltage_battery','current_battery','temp_battery','current_load','voltage_load','time','Capacity'] 43 | f1=['cycle','Capacity'] 44 | df=df[featurs] 45 | dataset=df[(df.Batt_name =='B0005')| (df.Batt_name =='B0006') | (df.Batt_name =='B0007')|(df.Batt_name =='B0018')] 46 | # Feature Selection 47 | dataset=dataset[(dataset['Batt_name']=='B0005')] 48 | dataset=dataset[f1] 49 | data_train=dataset[(dataset['cycle']<90)] 50 | data_set_train=data_train.iloc[:,1:2].values 51 | data_test=dataset[(dataset['cycle']>=90)] 52 | data_set_test=data_test.iloc[:,1:2].values 53 | 54 | data_train=data_train.values 55 | data_test=data_test.values 56 | from sklearn.preprocessing import MinMaxScaler 57 | sc=MinMaxScaler(feature_range=(0,1)) 58 | data_set_train=sc.fit_transform(data_set_train) 59 | data_set_test=sc.transform(data_set_test) 60 | 61 | X_train=[] 62 | y_train=[] 63 | for i in range(10,89): 64 | X_train.append(data_set_train[i-10:i,0]) 65 | y_train.append(data_set_train[i,0]) 66 | X_train,y_train=np.array(X_train),np.array(y_train) 67 | 68 | X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1)) 69 | 70 | regress=Sequential() 71 | regress.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1))) 72 | regress.add(Dropout(0.3)) 73 | 74 | regress.add(LSTM(units=50, return_sequences=True)) 75 | regress.add(Dropout(0.3)) 76 | 77 | 78 | regress.add(LSTM(units=50, return_sequences=True)) 79 | regress.add(Dropout(0.3)) 80 | 81 | regress.add(LSTM(units=50)) 82 | regress.add(Dropout(0.3)) 83 | 84 | 85 | regress.add(Dense(units=1)) 86 | 87 | regress.compile(optimizer='adam',loss='mean_squared_error') 88 | 89 | regress.fit(X_train,y_train,epochs=200,batch_size=50) 90 | 91 | ##### predictions 92 | data_total=pd.concat((data_train['Capacity'],data_test['Capacity']),axis=0) 93 | inputs=data_total[len(data_total)-len(data_test)-10:].values 94 | inputs=inputs.reshape(-1,1) 95 | inputs=sc.transform(inputs) 96 | 97 | X_test=[] 98 | for i in range(10,89): 99 | X_test.append(inputs[i-10:i,0]) 100 | X_test=np.array(X_test) 101 | X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1)) 102 | 103 | pred=regress.predict(X_test) 104 | pred=sc.inverse_transform(pred) 105 | pred=pred[:,0] 106 | tests=data_test.iloc[:,1:2] 107 | rmse = np.sqrt(mean_squared_error(tests, pred)) 108 | print('Test RMSE: %.3f' % rmse) 109 | 110 | metrics.r2_score(tests,pred) 111 | per=data_test 112 | per['per']=pred 113 | 114 | import seaborn as sns 115 | 116 | sns.set_style("darkgrid") 117 | plt.figure(figsize=(10, 5)) 118 | plt.plot(pred) 119 | plt.plot(sc.inverse_transform(data_set_test)) 120 | #Draw threshold 121 | plt.plot([0.0,70], [1.4, 1.4]) 122 | plt.ylabel('Capacity') 123 | # make x-axis ticks legible 124 | adf = plt.gca().get_xaxis().get_major_formatter() 125 | plt.xlabel('cycle') 126 | plt.title('Discharge B0005') 127 | plt.show() 128 | 129 | 130 | plt.plot(pred) 131 | plt.plot(sc.inverse_transform(data_set_test)) 132 | plt.show() -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Capstone Project 2 | 3 | The following was submitted as a final research project for a capstone course offered through the M.S. Computer Science at the University of Wisconsin-Milwaukee. 4 | 5 | 6 | NASA battery data analysis 7 | Data Structure: 8 | cycle: top level structure array containing the charge, discharge and impedance operations
    9 | 10 | type: operation type, can be charge, discharge or impedance. 11 | 12 | ambient_temperature: ambient temperature (degree C). 13 | 14 | time: the date and time of the start of the cycle, in MATLAB date vector format. 15 | 16 | data: data structure containing the measurements 17 | 18 | for charge the fields are: 19 | 20 | Voltage_measured: Battery terminal voltage (Volts) 21 | 22 | Current_measured: Battery output current (Amps) 23 | 24 | Temperature_measured: Battery temperature (degree C) 25 | 26 | Current_charge: Current measured at charger (Amps) 27 | 28 | Voltage_charge: Voltage measured at charger (Volts) 29 | 30 | Time: Time vector for the cycle (secs) 31 | 32 | for discharge the fields are: 33 | 34 | Voltage_measured: Battery terminal voltage (Volts) 35 | 36 | Current_measured: Battery output current (Amps) 37 | 38 | Temperature_measured: Battery temperature (degree C) 39 | 40 | Current_charge: Current measured at load (Amps) 41 | 42 | Voltage_charge: Voltage measured at load (Volts) 43 | 44 | Time: Time vector for the cycle (secs) 45 | 46 | Capacity: Battery capacity (Ahr) for discharge till 2.7V 47 | 48 | for impedance the fields are: 49 | 50 | Sense_current: Current in sense branch (Amps) 51 | 52 | Battery_current: Current in battery branch (Amps) 53 | 54 | Current_ratio: Ratio of the above currents 55 | 56 | Battery_impedance: Battery impedance (Ohms) computed from raw data 57 | 58 | Rectified_impedance: Calibrated and smoothed battery impedance (Ohms) 59 | 60 | Re: Estimated electrolyte resistance (Ohms) 61 | 62 | Rct: Estimated charge transfer resistance (Ohms) 63 | -------------------------------------------------------------------------------- /Real_Time_Pred/LSTM_Capacity_all_iter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "The code below used STLM by using only Capacity field to predict the RUL(STLM using one variable with multisteps) \n", 8 | "

    We built the model only on Battery B0005

    " 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [ 16 | { 17 | "name": "stderr", 18 | "output_type": "stream", 19 | "text": [ 20 | "Using TensorFlow backend.\n" 21 | ] 22 | } 23 | ], 24 | "source": [ 25 | "import sys \n", 26 | "import numpy as np # linear algebra\n", 27 | "from scipy.stats import randint\n", 28 | "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL\n", 29 | "import matplotlib.pyplot as plt # this is used for the plot the graph \n", 30 | "import seaborn as sns # used for plot interactive graph. \n", 31 | "from sklearn.model_selection import train_test_split # to split the data into two parts\n", 32 | "#from sklearn.cross_validation import KFold # use for cross validation\n", 33 | "from sklearn.preprocessing import StandardScaler # for normalization\n", 34 | "from sklearn.preprocessing import MinMaxScaler\n", 35 | "from sklearn.pipeline import Pipeline # pipeline making\n", 36 | "from sklearn.model_selection import cross_val_score\n", 37 | "from sklearn.feature_selection import SelectFromModel\n", 38 | "from sklearn import metrics # for the check the error and accuracy of the model\n", 39 | "from sklearn.metrics import mean_squared_error,r2_score\n", 40 | "from keras.callbacks import History \n", 41 | "## for Deep-learing:\n", 42 | "import keras\n", 43 | "from keras.layers import Dense\n", 44 | "from keras.models import Sequential\n", 45 | "from keras.utils import to_categorical\n", 46 | "from keras.optimizers import SGD \n", 47 | "from keras.callbacks import EarlyStopping\n", 48 | "from keras.utils import np_utils\n", 49 | "import itertools\n", 50 | "from keras.layers import LSTM\n", 51 | "from keras.layers.convolutional import Conv1D\n", 52 | "from keras.layers.convolutional import MaxPooling1D\n", 53 | "from keras.layers import Dropout" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 2, 59 | "metadata": {}, 60 | "outputs": [], 61 | "source": [ 62 | "df=pd.read_csv(\"B0005_discharge.csv\")\n", 63 | "#featurs=['Batt_name','cycle','amb_temp','voltage_battery','current_battery','temp_battery','current_load','voltage_load','time','Capacity','NewCap']\n", 64 | "f1=['cycle','Capacity']\n", 65 | "df=df[f1]\n", 66 | "#dataset=df[(df.Batt_name =='B0005')| (df.Batt_name =='B0006') | (df.Batt_name =='B0007')|(df.Batt_name =='B0018')]\n", 67 | "# Feature Selection\n", 68 | "#dataset=df[(df['Batt_name']=='B0006_11')]\n", 69 | "dataset=df#<70 need window to be 10\n", 70 | "#dataset=dataset[f1]\n", 71 | "train_num=91\n", 72 | "data_train=dataset[(dataset['cycle']=train_num)]\n", 75 | "data_set_test=data_test.iloc[:,1:2].values\n", 76 | "#Windoow Size after 90 could be 30 is a goood choise\n", 77 | "from sklearn.preprocessing import MinMaxScaler\n", 78 | "sc=MinMaxScaler(feature_range=(0,1))\n", 79 | "data_set_train=sc.fit_transform(data_set_train)\n", 80 | "data_set_test=sc.transform(data_set_test)\n", 81 | "\n", 82 | "X_train=[]\n", 83 | "y_train=[]\n", 84 | "window_size=5 #int(train_num/2)\n", 85 | "#cycle 82 ===> Wz:40==>pred: 78\n", 86 | "#take the last 10t to predict 10t+1\n", 87 | "#Cycle 110 ===> WZ:40 ===> batchz:32\n", 88 | "for i in range(window_size,train_num-1):\n", 89 | " X_train.append(data_set_train[i-window_size:i,0])\n", 90 | " y_train.append(data_set_train[i,0])\n", 91 | "X_train,y_train=np.array(X_train),np.array(y_train)\n", 92 | " \n", 93 | "X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 3, 99 | "metadata": {}, 100 | "outputs": [ 101 | { 102 | "name": "stdout", 103 | "output_type": "stream", 104 | "text": [ 105 | "85\n" 106 | ] 107 | } 108 | ], 109 | "source": [ 110 | "ln=len(data_train)\n", 111 | "ln\n", 112 | "print(len(X_train))" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "

    Applied STLM

    " 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 4, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "WARNING:tensorflow:From /home/khaled/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", 132 | "Instructions for updating:\n", 133 | "Colocations handled automatically by placer.\n", 134 | "WARNING:tensorflow:From /home/khaled/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", 135 | "Instructions for updating:\n", 136 | "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", 137 | "WARNING:tensorflow:From /home/khaled/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", 138 | "Instructions for updating:\n", 139 | "Use tf.cast instead.\n", 140 | "Epoch 1/100\n", 141 | "85/85 [==============================] - 9s 110ms/step - loss: 0.4268 - mean_squared_error: 0.4268\n", 142 | "Epoch 2/100\n", 143 | "85/85 [==============================] - 0s 3ms/step - loss: 0.2779 - mean_squared_error: 0.2779\n", 144 | "Epoch 3/100\n", 145 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0683 - mean_squared_error: 0.0683\n", 146 | "Epoch 4/100\n", 147 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0538 - mean_squared_error: 0.0538\n", 148 | "Epoch 5/100\n", 149 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0578 - mean_squared_error: 0.0578\n", 150 | "Epoch 6/100\n", 151 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0626 - mean_squared_error: 0.0626\n", 152 | "Epoch 7/100\n", 153 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0331 - mean_squared_error: 0.0331\n", 154 | "Epoch 8/100\n", 155 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0431 - mean_squared_error: 0.0431\n", 156 | "Epoch 9/100\n", 157 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0315 - mean_squared_error: 0.0315\n", 158 | "Epoch 10/100\n", 159 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0274 - mean_squared_error: 0.0274\n", 160 | "Epoch 11/100\n", 161 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0262 - mean_squared_error: 0.0262\n", 162 | "Epoch 12/100\n", 163 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0203 - mean_squared_error: 0.0203\n", 164 | "Epoch 13/100\n", 165 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0143 - mean_squared_error: 0.0143\n", 166 | "Epoch 14/100\n", 167 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0120 - mean_squared_error: 0.0120\n", 168 | "Epoch 15/100\n", 169 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n", 170 | "Epoch 16/100\n", 171 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n", 172 | "Epoch 17/100\n", 173 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0086 - mean_squared_error: 0.0086\n", 174 | "Epoch 18/100\n", 175 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0076 - mean_squared_error: 0.0076\n", 176 | "Epoch 19/100\n", 177 | "85/85 [==============================] - 0s 4ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n", 178 | "Epoch 20/100\n", 179 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 180 | "Epoch 21/100\n", 181 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n", 182 | "Epoch 22/100\n", 183 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n", 184 | "Epoch 23/100\n", 185 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 186 | "Epoch 24/100\n", 187 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 188 | "Epoch 25/100\n", 189 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 190 | "Epoch 26/100\n", 191 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n", 192 | "Epoch 27/100\n", 193 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n", 194 | "Epoch 28/100\n", 195 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n", 196 | "Epoch 29/100\n", 197 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n", 198 | "Epoch 30/100\n", 199 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n", 200 | "Epoch 31/100\n", 201 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n", 202 | "Epoch 32/100\n", 203 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 204 | "Epoch 33/100\n", 205 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 206 | "Epoch 34/100\n", 207 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 208 | "Epoch 35/100\n", 209 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 210 | "Epoch 36/100\n", 211 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 212 | "Epoch 37/100\n", 213 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0076 - mean_squared_error: 0.0076\n", 214 | "Epoch 38/100\n", 215 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 216 | "Epoch 39/100\n", 217 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 218 | "Epoch 40/100\n", 219 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 220 | "Epoch 41/100\n", 221 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 222 | "Epoch 42/100\n", 223 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 224 | "Epoch 43/100\n", 225 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 226 | "Epoch 44/100\n", 227 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 228 | "Epoch 45/100\n", 229 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 230 | "Epoch 46/100\n", 231 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0071 - mean_squared_error: 0.0071\n", 232 | "Epoch 47/100\n", 233 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0077 - mean_squared_error: 0.0077\n", 234 | "Epoch 48/100\n", 235 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n", 236 | "Epoch 49/100\n", 237 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 238 | "Epoch 50/100\n", 239 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 240 | "Epoch 51/100\n", 241 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n", 242 | "Epoch 52/100\n", 243 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n", 244 | "Epoch 53/100\n", 245 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n", 246 | "Epoch 54/100\n", 247 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 248 | "Epoch 55/100\n", 249 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 250 | "Epoch 56/100\n", 251 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n", 252 | "Epoch 57/100\n", 253 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 254 | "Epoch 58/100\n", 255 | "85/85 [==============================] - 0s 5ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n", 256 | "Epoch 59/100\n", 257 | "85/85 [==============================] - 0s 4ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n", 258 | "Epoch 60/100\n", 259 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 260 | "Epoch 61/100\n", 261 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n", 262 | "Epoch 62/100\n", 263 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0077 - mean_squared_error: 0.0077\n", 264 | "Epoch 63/100\n", 265 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0082 - mean_squared_error: 0.0082\n", 266 | "Epoch 64/100\n", 267 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 268 | "Epoch 65/100\n", 269 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n", 270 | "Epoch 66/100\n", 271 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0072 - mean_squared_error: 0.0072\n", 272 | "Epoch 67/100\n" 273 | ] 274 | }, 275 | { 276 | "name": "stdout", 277 | "output_type": "stream", 278 | "text": [ 279 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0074 - mean_squared_error: 0.0074\n", 280 | "Epoch 68/100\n", 281 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 282 | "Epoch 69/100\n", 283 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 284 | "Epoch 70/100\n", 285 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n", 286 | "Epoch 71/100\n", 287 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0046 - mean_squared_error: 0.0046\n", 288 | "Epoch 72/100\n", 289 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 290 | "Epoch 73/100\n", 291 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n", 292 | "Epoch 74/100\n", 293 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 294 | "Epoch 75/100\n", 295 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n", 296 | "Epoch 76/100\n", 297 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n", 298 | "Epoch 77/100\n", 299 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 300 | "Epoch 78/100\n", 301 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 302 | "Epoch 79/100\n", 303 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 304 | "Epoch 80/100\n", 305 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n", 306 | "Epoch 81/100\n", 307 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 308 | "Epoch 82/100\n", 309 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 310 | "Epoch 83/100\n", 311 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 312 | "Epoch 84/100\n", 313 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0063 - mean_squared_error: 0.0063\n", 314 | "Epoch 85/100\n", 315 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 316 | "Epoch 86/100\n", 317 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 318 | "Epoch 87/100\n", 319 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 320 | "Epoch 88/100\n", 321 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 322 | "Epoch 89/100\n", 323 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 324 | "Epoch 90/100\n", 325 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0042 - mean_squared_error: 0.0042\n", 326 | "Epoch 91/100\n", 327 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n", 328 | "Epoch 92/100\n", 329 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 330 | "Epoch 93/100\n", 331 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 332 | "Epoch 94/100\n", 333 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n", 334 | "Epoch 95/100\n", 335 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0047 - mean_squared_error: 0.0047\n", 336 | "Epoch 96/100\n", 337 | "85/85 [==============================] - 0s 4ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 338 | "Epoch 97/100\n", 339 | "85/85 [==============================] - 0s 2ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 340 | "Epoch 98/100\n", 341 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 342 | "Epoch 99/100\n", 343 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 344 | "Epoch 100/100\n", 345 | "85/85 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n" 346 | ] 347 | } 348 | ], 349 | "source": [ 350 | "regress=Sequential()\n", 351 | "from keras import regularizers\n", 352 | "regress.add(LSTM(units=200, return_sequences=True, input_shape=(X_train.shape[1],1)))\n", 353 | "regress.add(Dropout(0.3))\n", 354 | "\n", 355 | "regress.add(LSTM(units=200, return_sequences=True))\n", 356 | "regress.add(Dropout(0.3))\n", 357 | "\n", 358 | "\n", 359 | "regress.add(LSTM(units=200, return_sequences=True))\n", 360 | "regress.add(Dropout(0.3))\n", 361 | "\n", 362 | "regress.add(LSTM(units=200))\n", 363 | "regress.add(Dropout(0.3))\n", 364 | "\n", 365 | "\n", 366 | "regress.add(Dense(units=1))\n", 367 | "\n", 368 | "regress.compile(optimizer='adam',loss='mean_squared_error',metrics=['mse'])\n", 369 | "\n", 370 | "hist=regress.fit(X_train,y_train,epochs=100,batch_size=30)\n" 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 9, 376 | "metadata": {}, 377 | "outputs": [], 378 | "source": [ 379 | "#function to calculate the sum of the difference between two cycles.\n", 380 | "def diff_avg(Data_set):\n", 381 | " data_sum=Data_set.iloc[:,1:2]\n", 382 | " avg=0\n", 383 | " init=data_sum['Capacity'][0]\n", 384 | " substr=0.0\n", 385 | " calcul=0.0\n", 386 | " for i in range(1,len(data_sum)):\n", 387 | " substr=init-data_sum['Capacity'][i]\n", 388 | " calcul=calcul+ substr\n", 389 | " init=data_sum['Capacity'][i]\n", 390 | " avg=calcul/len(data_sum)\n", 391 | " return (avg)\n", 392 | " \n" 393 | ] 394 | }, 395 | { 396 | "cell_type": "code", 397 | "execution_count": 5, 398 | "metadata": {}, 399 | "outputs": [], 400 | "source": [ 401 | "#sc=MinMaxScaler(feature_range=(0,1))\n", 402 | "def Scale_X(dataset,lag):#return the 3 D array for training and 1 D to actual value.\n", 403 | " # from sklearn.preprocessing import MinMaxScaler\n", 404 | " data=dataset.iloc[:,1:2].values\n", 405 | " data=sc.fit_transform(data)\n", 406 | " X_train=[]\n", 407 | " y_train=[]\n", 408 | " window_size=lag\n", 409 | " train_num=len(data)\n", 410 | " for i in range(window_size,train_num-1):\n", 411 | " X_train.append(data[i-window_size:i,0])\n", 412 | " y_train.append(data[i,0])\n", 413 | " X_train,y_train=np.array(X_train),np.array(y_train)\n", 414 | " X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n", 415 | " return X_train, y_train" 416 | ] 417 | }, 418 | { 419 | "cell_type": "markdown", 420 | "metadata": {}, 421 | "source": [ 422 | "

    Test the Model that was built by STLM

    " 423 | ] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "execution_count": 6, 428 | "metadata": {}, 429 | "outputs": [ 430 | { 431 | "name": "stdout", 432 | "output_type": "stream", 433 | "text": [ 434 | "95\n", 435 | "0.016773545182967458\n", 436 | "0.005426093552480726\n" 437 | ] 438 | } 439 | ], 440 | "source": [ 441 | "len(data_test)\n", 442 | "num_cycle_pred=5\n", 443 | "cycles=len(data_train)+num_cycle_pred\n", 444 | "print(cycles)\n", 445 | "regress.loss\n", 446 | "training_loss = hist.history['loss']\n", 447 | "xx=0\n", 448 | "for i in range(len(training_loss)):\n", 449 | " xx=xx+training_loss[i]\n", 450 | "avg=xx/len(training_loss)\n", 451 | "print(avg)\n", 452 | "loss_train=training_loss[len(training_loss)-1]\n", 453 | "ini_val=loss_train\n", 454 | "print(loss_train)" 455 | ] 456 | }, 457 | { 458 | "cell_type": "code", 459 | "execution_count": 7, 460 | "metadata": {}, 461 | "outputs": [], 462 | "source": [ 463 | "#### predictions\n", 464 | "#data_total=pd.concat((data_train['NewCap'],data_test['NewCap']),axis=0)\n", 465 | "#inputs=data_total[len(data_total)-len(data_test)-10:].values\n", 466 | "#inputs=inputs.reshape(-1,1)\n" 467 | ] 468 | }, 469 | { 470 | "cell_type": "code", 471 | "execution_count": 10, 472 | "metadata": {}, 473 | "outputs": [ 474 | { 475 | "name": "stdout", 476 | "output_type": "stream", 477 | "text": [ 478 | "85 1.527914\n", 479 | "86 1.528525\n", 480 | "87 1.522647\n", 481 | "88 1.517486\n", 482 | "89 1.605819\n", 483 | "Name: Capacity, dtype: float64\n", 484 | "[1.5144728]\n", 485 | "86 1.528525\n", 486 | "87 1.522647\n", 487 | "88 1.517486\n", 488 | "89 1.605819\n", 489 | "90 1.514473\n", 490 | "dtype: float64\n", 491 | "[1.5170808]\n", 492 | "87 1.522647\n", 493 | "88 1.517486\n", 494 | "89 1.605819\n", 495 | "90 1.514473\n", 496 | "91 1.517081\n", 497 | "dtype: float64\n", 498 | "[1.5206444]\n", 499 | "88 1.517486\n", 500 | "89 1.605819\n", 501 | "90 1.514473\n", 502 | "91 1.517081\n", 503 | "92 1.520644\n", 504 | "dtype: float64\n", 505 | "[1.5274547]\n", 506 | "89 1.605819\n", 507 | "90 1.514473\n", 508 | "91 1.517081\n", 509 | "92 1.520644\n", 510 | "93 1.527455\n", 511 | "dtype: float64\n", 512 | "[1.5371673]\n", 513 | "Epoch 1/100\n", 514 | "89/89 [==============================] - 1s 9ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 515 | "Epoch 2/100\n", 516 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n", 517 | "Epoch 3/100\n", 518 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 519 | "Epoch 4/100\n", 520 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0062 - mean_squared_error: 0.0062\n", 521 | "Epoch 5/100\n", 522 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n", 523 | "Epoch 6/100\n", 524 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0067 - mean_squared_error: 0.0067\n", 525 | "Epoch 7/100\n", 526 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 527 | "Epoch 8/100\n", 528 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 529 | "Epoch 9/100\n", 530 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0070 - mean_squared_error: 0.0070\n", 531 | "Epoch 10/100\n", 532 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 533 | "Epoch 11/100\n", 534 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 535 | "Epoch 12/100\n", 536 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n", 537 | "Epoch 13/100\n", 538 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n", 539 | "Epoch 14/100\n", 540 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 541 | "Epoch 15/100\n", 542 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 543 | "Epoch 16/100\n", 544 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0043 - mean_squared_error: 0.0043\n", 545 | "Epoch 17/100\n", 546 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 547 | "Epoch 18/100\n", 548 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 549 | "Epoch 19/100\n", 550 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 551 | "Epoch 20/100\n", 552 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0049 - mean_squared_error: 0.0049\n", 553 | "Epoch 21/100\n", 554 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n", 555 | "Epoch 22/100\n", 556 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 557 | "Epoch 23/100\n", 558 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0049 - mean_squared_error: 0.0049\n", 559 | "Epoch 24/100\n", 560 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0049 - mean_squared_error: 0.0049\n", 561 | "Epoch 25/100\n", 562 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0061 - mean_squared_error: 0.0061\n", 563 | "Epoch 26/100\n", 564 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 565 | "Epoch 27/100\n", 566 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0038 - mean_squared_error: 0.0038\n", 567 | "Epoch 28/100\n", 568 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n", 569 | "Epoch 29/100\n", 570 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0064 - mean_squared_error: 0.0064\n", 571 | "Epoch 30/100\n", 572 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0044 - mean_squared_error: 0.0044\n", 573 | "Epoch 31/100\n", 574 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0038 - mean_squared_error: 0.0038\n", 575 | "Epoch 32/100\n", 576 | "89/89 [==============================] - 0s 4ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 577 | "Epoch 33/100\n", 578 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 579 | "Epoch 34/100\n", 580 | "89/89 [==============================] - 0s 4ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 581 | "Epoch 35/100\n", 582 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 583 | "Epoch 36/100\n", 584 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 585 | "Epoch 37/100\n", 586 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0049 - mean_squared_error: 0.0049\n", 587 | "Epoch 38/100\n", 588 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 589 | "Epoch 39/100\n", 590 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 591 | "Epoch 40/100\n", 592 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 593 | "Epoch 41/100\n", 594 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0038 - mean_squared_error: 0.0038\n", 595 | "Epoch 42/100\n", 596 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 597 | "Epoch 43/100\n", 598 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 599 | "Epoch 44/100\n", 600 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 601 | "Epoch 45/100\n", 602 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0053 - mean_squared_error: 0.0053\n", 603 | "Epoch 46/100\n", 604 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 605 | "Epoch 47/100\n", 606 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0054 - mean_squared_error: 0.0054\n", 607 | "Epoch 48/100\n", 608 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 609 | "Epoch 49/100\n", 610 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 611 | "Epoch 50/100\n", 612 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0044 - mean_squared_error: 0.0044\n", 613 | "Epoch 51/100\n", 614 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 615 | "Epoch 52/100\n", 616 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0060 - mean_squared_error: 0.0060\n", 617 | "Epoch 53/100\n", 618 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0047 - mean_squared_error: 0.0047\n", 619 | "Epoch 54/100\n", 620 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n", 621 | "Epoch 55/100\n", 622 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 623 | "Epoch 56/100\n", 624 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0069 - mean_squared_error: 0.0069\n", 625 | "Epoch 57/100\n", 626 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0081 - mean_squared_error: 0.0081\n", 627 | "Epoch 58/100\n", 628 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0065 - mean_squared_error: 0.0065\n", 629 | "Epoch 59/100\n", 630 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0073 - mean_squared_error: 0.0073\n", 631 | "Epoch 60/100\n", 632 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 633 | "Epoch 61/100\n", 634 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0066 - mean_squared_error: 0.0066\n", 635 | "Epoch 62/100\n", 636 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 637 | "Epoch 63/100\n", 638 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 639 | "Epoch 64/100\n", 640 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 641 | "Epoch 65/100\n", 642 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 643 | "Epoch 66/100\n", 644 | "89/89 [==============================] - 0s 4ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n", 645 | "Epoch 67/100\n", 646 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0039 - mean_squared_error: 0.0039\n", 647 | "Epoch 68/100\n", 648 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0048 - mean_squared_error: 0.0048\n", 649 | "Epoch 69/100\n", 650 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0044 - mean_squared_error: 0.0044\n", 651 | "Epoch 70/100\n" 652 | ] 653 | }, 654 | { 655 | "name": "stdout", 656 | "output_type": "stream", 657 | "text": [ 658 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0041 - mean_squared_error: 0.0041\n", 659 | "Epoch 71/100\n", 660 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0046 - mean_squared_error: 0.0046\n", 661 | "Epoch 72/100\n", 662 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0046 - mean_squared_error: 0.0046\n", 663 | "Epoch 73/100\n", 664 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n", 665 | "Epoch 74/100\n", 666 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n", 667 | "Epoch 75/100\n", 668 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 669 | "Epoch 76/100\n", 670 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 671 | "Epoch 77/100\n", 672 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 673 | "Epoch 78/100\n", 674 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0056 - mean_squared_error: 0.0056\n", 675 | "Epoch 79/100\n", 676 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0046 - mean_squared_error: 0.0046\n", 677 | "Epoch 80/100\n", 678 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 679 | "Epoch 81/100\n", 680 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0044 - mean_squared_error: 0.0044\n", 681 | "Epoch 82/100\n", 682 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0042 - mean_squared_error: 0.0042\n", 683 | "Epoch 83/100\n", 684 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0037 - mean_squared_error: 0.0037\n", 685 | "Epoch 84/100\n", 686 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0045 - mean_squared_error: 0.0045\n", 687 | "Epoch 85/100\n", 688 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0047 - mean_squared_error: 0.0047\n", 689 | "Epoch 86/100\n", 690 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 691 | "Epoch 87/100\n", 692 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0045 - mean_squared_error: 0.0045\n", 693 | "Epoch 88/100\n", 694 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0068 - mean_squared_error: 0.0068\n", 695 | "Epoch 89/100\n", 696 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0055 - mean_squared_error: 0.0055\n", 697 | "Epoch 90/100\n", 698 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0043 - mean_squared_error: 0.0043\n", 699 | "Epoch 91/100\n", 700 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0051 - mean_squared_error: 0.0051\n", 701 | "Epoch 92/100\n", 702 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0043 - mean_squared_error: 0.0043\n", 703 | "Epoch 93/100\n", 704 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0052 - mean_squared_error: 0.0052\n", 705 | "Epoch 94/100\n", 706 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0040 - mean_squared_error: 0.0040\n", 707 | "Epoch 95/100\n", 708 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0050 - mean_squared_error: 0.0050\n", 709 | "Epoch 96/100\n", 710 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0059 - mean_squared_error: 0.0059\n", 711 | "Epoch 97/100\n", 712 | "89/89 [==============================] - 0s 5ms/step - loss: 0.0057 - mean_squared_error: 0.0057\n", 713 | "Epoch 98/100\n", 714 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0044 - mean_squared_error: 0.0044\n", 715 | "Epoch 99/100\n", 716 | "89/89 [==============================] - 0s 2ms/step - loss: 0.0075 - mean_squared_error: 0.0075\n", 717 | "Epoch 100/100\n", 718 | "89/89 [==============================] - 0s 3ms/step - loss: 0.0058 - mean_squared_error: 0.0058\n" 719 | ] 720 | } 721 | ], 722 | "source": [ 723 | "#### predictions, firstly we predict the next 5 cycles, then we add these cycles to the training dataset\n", 724 | "### After that, we retrain the model with t+1,t+2,t3,t4,t5. and we do again to predict the t6 -t10 and so on.\n", 725 | "k=0\n", 726 | "while k<1:\n", 727 | " \n", 728 | " data_total=data_train['Capacity']\n", 729 | " avg=diff_avg(data_train)\n", 730 | " test=[]\n", 731 | " l=[]\n", 732 | " test=data_total[len(data_total)-window_size:]\n", 733 | " #print(test)\n", 734 | " #Make prediction to the next 5 cycles\n", 735 | " for i in range(num_cycle_pred):\n", 736 | " inputs1=test[len(test)-window_size-i:]\n", 737 | " inputs=test[len(test)-window_size-i:].values\n", 738 | " inputs=inputs.reshape(-1,1)\n", 739 | " inputs=sc.transform(inputs)\n", 740 | " X_test=[] \n", 741 | " X_test.append(inputs[i:window_size+i,0])\n", 742 | " X_test=np.array(X_test)\n", 743 | " X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))\n", 744 | " pred=regress.predict(X_test)\n", 745 | " pred=sc.inverse_transform(pred)\n", 746 | " pred=pred[:,0]\n", 747 | " \n", 748 | " if loss_train>= 0.0199:#this filter used only when the error 0.02 or above,(by assumption)\n", 749 | " xx=float(pred)-avg\n", 750 | " else:\n", 751 | " xx=float(pred)#-avg\n", 752 | " if i <= 5:\n", 753 | " just_test=inputs1[i:window_size+i]\n", 754 | " print(just_test)\n", 755 | " print(pred)\n", 756 | " ###########End of prediction###################\n", 757 | " ################################################\n", 758 | " #if loss_train>= 0.017:\n", 759 | " # xx=float(pred)-avg#loss_train\n", 760 | " #elif ini_val>=1.07 and loss_train<0.017:\n", 761 | " # xx=float(pred)+loss_train \n", 762 | " #else:\n", 763 | " # xx=float(pred)\n", 764 | " l.append(float(xx))\n", 765 | " y=test.index[len(test)-1]\n", 766 | " s1 = pd.Series(float(xx),index=[y+1])# convert the new prodiction to time series platform\n", 767 | " test=pd.concat([test,s1])\n", 768 | " j=len(data_train)+1\n", 769 | " s2=pd.Series([int(j),float(xx)],index=['cycle','Capacity'])# add new prediction to the Training dataset\n", 770 | " data_train=data_train.append(s2,ignore_index=True)\n", 771 | " X_train,y_train=Scale_X(data_train,window_size)# Scaling the training dataset with new cycles.\n", 772 | " hist=regress.fit(X_train,y_train,epochs=100,batch_size=30)# rebuild the model \n", 773 | " training_loss = hist.history['loss']\n", 774 | " avg=diff_avg(data_train)# find avg(for thesum of the difference between two cycles)\n", 775 | " loss_train=training_loss[len(training_loss)-1]# get the RMSE\n", 776 | " k=k+1\n" 777 | ] 778 | } 779 | ], 780 | "metadata": { 781 | "kernelspec": { 782 | "display_name": "Python 3", 783 | "language": "python", 784 | "name": "python3" 785 | }, 786 | "language_info": { 787 | "codemirror_mode": { 788 | "name": "ipython", 789 | "version": 3 790 | }, 791 | "file_extension": ".py", 792 | "mimetype": "text/x-python", 793 | "name": "python", 794 | "nbconvert_exporter": "python", 795 | "pygments_lexer": "ipython3", 796 | "version": "3.7.3" 797 | } 798 | }, 799 | "nbformat": 4, 800 | "nbformat_minor": 2 801 | } 802 | -------------------------------------------------------------------------------- /Real_Time_Pred/Untitled Diagram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kalrfou/CapstoneProject/82409783f9fc9af84867fc690386c8d8f602dc94/Real_Time_Pred/Untitled Diagram.png -------------------------------------------------------------------------------- /Real_Time_Pred/log: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /STLM_onevar_multiSteps.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "The code below used STLM by using only Capacity field to predict the RUL(STLM using one variable with multisteps) \n", 8 | "

    We built the model only on Battery B0005

    " 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 11, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "import sys \n", 18 | "import numpy as np # linear algebra\n", 19 | "from scipy.stats import randint\n", 20 | "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL\n", 21 | "import matplotlib.pyplot as plt # this is used for the plot the graph \n", 22 | "import seaborn as sns # used for plot interactive graph. \n", 23 | "from sklearn.model_selection import train_test_split # to split the data into two parts\n", 24 | "#from sklearn.cross_validation import KFold # use for cross validation\n", 25 | "from sklearn.preprocessing import StandardScaler # for normalization\n", 26 | "from sklearn.preprocessing import MinMaxScaler\n", 27 | "from sklearn.feature_selection import SelectFromModel\n", 28 | "from sklearn import metrics # for the check the error and accuracy of the model\n", 29 | "from sklearn.metrics import mean_squared_error,r2_score\n", 30 | "\n", 31 | "\n", 32 | "\n", 33 | "\n", 34 | "df=pd.read_csv(\"discharge_onerow.csv\")\n", 35 | "featurs=['Batt_name','cycle','amb_temp','voltage_battery','current_battery','temp_battery','current_load','voltage_load','time','Capacity']\n", 36 | "f1=['cycle','Capacity']\n", 37 | "df=df[featurs]\n", 38 | "dataset=df[(df.Batt_name =='B0005')| (df.Batt_name =='B0006') | (df.Batt_name =='B0007')|(df.Batt_name =='B0018')]\n", 39 | "# Feature Selection\n", 40 | "dataset=dataset[(dataset['Batt_name']=='B0005')]\n", 41 | "dataset=dataset[f1]\n", 42 | "data_train=dataset[(dataset['cycle']<90)]\n", 43 | "data_set_train=data_train.iloc[:,1:2].values\n", 44 | "data_test=dataset[(dataset['cycle']>=90)]\n", 45 | "data_set_test=data_test.iloc[:,1:2].values\n", 46 | "\n", 47 | "from sklearn.preprocessing import MinMaxScaler\n", 48 | "sc=MinMaxScaler(feature_range=(0,1))\n", 49 | "data_set_train=sc.fit_transform(data_set_train)\n", 50 | "data_set_test=sc.transform(data_set_test)\n", 51 | "\n", 52 | "X_train=[]\n", 53 | "y_train=[]\n", 54 | "#take the last 10t to predict 10t+1\n", 55 | "for i in range(10,89):\n", 56 | " X_train.append(data_set_train[i-10:i,0])\n", 57 | " y_train.append(data_set_train[i,0])\n", 58 | "X_train,y_train=np.array(X_train),np.array(y_train)\n", 59 | " \n", 60 | "X_train=np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))\n" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | " Applied STLM " 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": null, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "regress=Sequential()\n", 77 | "regress.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1)))\n", 78 | "regress.add(Dropout(0.3))\n", 79 | "\n", 80 | "regress.add(LSTM(units=50, return_sequences=True))\n", 81 | "regress.add(Dropout(0.3))\n", 82 | "\n", 83 | "\n", 84 | "regress.add(LSTM(units=50, return_sequences=True))\n", 85 | "regress.add(Dropout(0.3))\n", 86 | "\n", 87 | "regress.add(LSTM(units=50))\n", 88 | "regress.add(Dropout(0.3))\n", 89 | "\n", 90 | "\n", 91 | "regress.add(Dense(units=1))\n", 92 | "\n", 93 | "regress.compile(optimizer='adam',loss='mean_squared_error')\n", 94 | "\n", 95 | "regress.fit(X_train,y_train,epochs=200,batch_size=50)\n" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "Epoch 1/100\n", 103 | "79/79 [==============================] - 3s 43ms/step - loss: 0.4145\n", 104 | "\n", 105 | "Epoch 2/100\n", 106 | "79/79 [==============================] - 0s 924us/step - loss: 0.3334\n", 107 | "\n", 108 | "Epoch 3/100\n", 109 | "79/79 [==============================] - 0s 904us/step - loss: 0.2314\n", 110 | "\n", 111 | "Epoch 4/100\n", 112 | "79/79 [==============================] - 0s 915us/step - loss: 0.1041\n", 113 | "\n", 114 | "Epoch 5/100\n", 115 | "79/79 [==============================] - 0s 928us/step - loss: 0.0579\n", 116 | "\n", 117 | "Epoch 6/100\n", 118 | "79/79 [==============================] - 0s 934us/step - loss: 0.0703\n", 119 | "\n", 120 | "Epoch 7/100\n", 121 | "79/79 [==============================] - 0s 917us/step - loss: 0.0401\n", 122 | "\n", 123 | "Epoch 8/100\n", 124 | "79/79 [==============================] - 0s 904us/step - loss: 0.0533\n", 125 | "\n", 126 | "Epoch 9/100\n", 127 | "79/79 [==============================] - 0s 925us/step - loss: 0.0419\n", 128 | "\n", 129 | "Epoch 10/100\n", 130 | "79/79 [==============================] - 0s 905us/step - loss: 0.0424\n", 131 | "\n", 132 | "Epoch 11/100\n", 133 | "79/79 [==============================] - 0s 909us/step - loss: 0.0375\n", 134 | "\n", 135 | "Epoch 12/100\n", 136 | "79/79 [==============================] - 0s 893us/step - loss: 0.0414\n", 137 | "\n", 138 | "Epoch 13/100\n", 139 | "79/79 [==============================] - 0s 904us/step - loss: 0.0325\n", 140 | "\n", 141 | "Epoch 14/100\n", 142 | "79/79 [==============================] - 0s 933us/step - loss: 0.0275\n", 143 | "\n", 144 | "Epoch 15/100\n", 145 | "79/79 [==============================] - 0s 873us/step - loss: 0.0318\n", 146 | "\n", 147 | "Epoch 16/100\n", 148 | "79/79 [==============================] - 0s 918us/step - loss: 0.0192\n", 149 | "\n", 150 | "Epoch 17/100\n", 151 | "79/79 [==============================] - 0s 918us/step - loss: 0.0207\n", 152 | "\n", 153 | "Epoch 18/100\n", 154 | "79/79 [==============================] - 0s 909us/step - loss: 0.0172\n", 155 | "\n", 156 | "Epoch 19/100\n", 157 | "79/79 [==============================] - 0s 901us/step - loss: 0.0186\n", 158 | "\n", 159 | "Epoch 20/100\n", 160 | "79/79 [==============================] - 0s 922us/step - loss: 0.0141\n", 161 | "\n", 162 | "Epoch 21/100\n", 163 | "79/79 [==============================] - 0s 920us/step - loss: 0.0172\n", 164 | "\n", 165 | "Epoch 22/100\n", 166 | "79/79 [==============================] - 0s 903us/step - loss: 0.0123\n", 167 | "\n", 168 | "Epoch 23/100\n", 169 | "79/79 [==============================] - 0s 904us/step - loss: 0.0131\n", 170 | "\n", 171 | "Epoch 24/100\n", 172 | "79/79 [==============================] - 0s 885us/step - loss: 0.0113\n", 173 | "\n", 174 | "Epoch 25/100\n", 175 | "79/79 [==============================] - 0s 914us/step - loss: 0.0123\n", 176 | "\n", 177 | "Epoch 26/100\n", 178 | "79/79 [==============================] - 0s 919us/step - loss: 0.0127\n", 179 | "\n", 180 | "Epoch 27/100\n", 181 | "79/79 [==============================] - 0s 930us/step - loss: 0.0107\n", 182 | "\n", 183 | "Epoch 28/100\n", 184 | "79/79 [==============================] - 0s 928us/step - loss: 0.0131\n", 185 | "\n", 186 | "Epoch 29/100\n", 187 | "79/79 [==============================] - 0s 924us/step - loss: 0.0168\n", 188 | "\n", 189 | "Epoch 30/100\n", 190 | "79/79 [==============================] - 0s 896us/step - loss: 0.0104\n", 191 | "\n", 192 | ".\n", 193 | ".\n", 194 | ".\n", 195 | ".\n", 196 | "\n", 197 | "Epoch 100/100\n", 198 | "79/79 [==============================] - 0s 889us/step - loss: 0.0091\n", 199 | "\n", 200 | "Out[213]: " 201 | ] 202 | }, 203 | { 204 | "cell_type": "markdown", 205 | "metadata": {}, 206 | "source": [ 207 | " Test the Model that was built by STLM" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 2, 213 | "metadata": {}, 214 | "outputs": [], 215 | "source": [ 216 | "#### predictions\n", 217 | "data_total=pd.concat((data_train['Capacity'],data_test['Capacity']),axis=0)\n", 218 | "inputs=data_total[len(data_total)-len(data_test)-10:].values\n", 219 | "inputs=inputs.reshape(-1,1)\n", 220 | "inputs=sc.transform(inputs)" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": null, 226 | "metadata": {}, 227 | "outputs": [], 228 | "source": [ 229 | "X_test=[]\n", 230 | "for i in range(10,89):\n", 231 | " X_test.append(inputs[i-10:i,0])\n", 232 | "X_test=np.array(X_test)\n", 233 | "X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))\n", 234 | "\n", 235 | "pred=regress.predict(X_test)\n", 236 | "pred=sc.inverse_transform(pred)\n", 237 | "pred=pred[:,0]\n", 238 | "tests=data_test.iloc[:,1:2]\n", 239 | "rmse = np.sqrt(mean_squared_error(tests, pred))\n", 240 | "print('Test RMSE: %.3f' % rmse)\n", 241 | "\n", 242 | "metrics.r2_score(tests,pred)" 243 | ] 244 | }, 245 | { 246 | "cell_type": "markdown", 247 | "metadata": {}, 248 | "source": [ 249 | "Test RMSE: 0.030\n", 250 | "\n", 251 | "\n", 252 | "R^2: 0.8916473658827782" 253 | ] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "metadata": {}, 258 | "source": [ 259 | "RUL(Error)=|126-125|\n", 260 | "=1 cycle " 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 7, 266 | "metadata": {}, 267 | "outputs": [], 268 | "source": [ 269 | "pred4=[1.5109049, 1.5067059, 1.5034488, 1.5005403, 1.498761 , 1.4981421,\n", 270 | " 1.4992642, 1.5018138, 1.503656 , 1.5056738, 1.5075662, 1.492796 ,\n", 271 | " 1.4839845, 1.4767292, 1.4711597, 1.465779 , 1.4615036, 1.4575909,\n", 272 | " 1.454027 , 1.4506783, 1.4484158, 1.4462972, 1.4443446, 1.4424661,\n", 273 | " 1.4380679, 1.4311802, 1.425846 , 1.4214693, 1.4188418, 1.4155321,\n", 274 | " 1.4114283, 1.4077026, 1.405254 , 1.4034641, 1.4015648, 1.4002702,\n", 275 | " 1.3994751, 1.3991467, 1.3991414, 1.3986106, 1.3982402, 1.3933313,\n", 276 | " 1.3868399, 1.3825758, 1.3791816, 1.3761727, 1.3734736, 1.3712626,\n", 277 | " 1.3694792, 1.3682021, 1.3673415, 1.3668255, 1.3659699, 1.3655413,\n", 278 | " 1.3632015, 1.3588042, 1.3560386, 1.3534404, 1.3517599, 1.3495598,\n", 279 | " 1.3468499, 1.3443987, 1.3423759, 1.3409755, 1.3396008, 1.3388804,\n", 280 | " 1.3388563, 1.3395094, 1.340758 , 1.3418714, 1.3428435, 1.3428377,\n", 281 | " 1.3375453, 1.3343163, 1.3319844, 1.3299532, 1.3282046, 1.3267752,\n", 282 | " 1.3251127]" 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": 3, 288 | "metadata": {}, 289 | "outputs": [], 290 | "source": [ 291 | "pred2=[1.5095047, 1.505182 , 1.5017349, 1.4985278, 1.4964548, 1.4956222,\n", 292 | " 1.4967338, 1.4995174, 1.5016944, 1.5042136, 1.5067403, 1.4912944,\n", 293 | " 1.482186 , 1.4747189, 1.4690324, 1.4634969, 1.4590889, 1.4550277,\n", 294 | " 1.4513285, 1.4478728, 1.4456096, 1.4435389, 1.4416875, 1.4399569,\n", 295 | " 1.435547 , 1.4284523, 1.4229819, 1.4185165, 1.4159185, 1.412598 ,\n", 296 | " 1.4084224, 1.4046086, 1.4020915, 1.4002208, 1.3982018, 1.3968226,\n", 297 | " 1.3960024, 1.3957292, 1.3958644, 1.395504 , 1.3953686, 1.3904271,\n", 298 | " 1.3837754, 1.379454 , 1.3760293, 1.3729705, 1.370195 , 1.367903 ,\n", 299 | " 1.3660536, 1.3647503, 1.3639095, 1.3634626, 1.3626862, 1.3623906,\n", 300 | " 1.3600874, 1.3555952, 1.3528095, 1.3501998, 1.348567 , 1.346395 ,\n", 301 | " 1.3436819, 1.3412246, 1.3391671, 1.3377022, 1.3362108, 1.3353803,\n", 302 | " 1.3352875, 1.3359427, 1.3372804, 1.3385375, 1.3397032, 1.3398769,\n", 303 | " 1.3344688, 1.331196 , 1.3288473, 1.326803 , 1.3250507, 1.3236343,\n", 304 | " 1.3219578]" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": 9, 310 | "metadata": {}, 311 | "outputs": [ 312 | { 313 | "name": "stdout", 314 | "output_type": "stream", 315 | "text": [ 316 | " cycle Capacity pre\n", 317 | "469 90 1.605819 1.510905\n", 318 | "470 91 1.563849 1.506706\n", 319 | "471 92 1.548092 1.503449\n", 320 | "472 93 1.532376 1.500540\n", 321 | "473 94 1.526953 1.498761\n" 322 | ] 323 | }, 324 | { 325 | "name": "stderr", 326 | "output_type": "stream", 327 | "text": [ 328 | "/home/khaled/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", 329 | "A value is trying to be set on a copy of a slice from a DataFrame.\n", 330 | "Try using .loc[row_indexer,col_indexer] = value instead\n", 331 | "\n", 332 | "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", 333 | " \n" 334 | ] 335 | } 336 | ], 337 | "source": [ 338 | "len(pred4)\n", 339 | "data_test['pre']=pred4\n", 340 | "print(data_test.head())" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": 10, 346 | "metadata": {}, 347 | "outputs": [ 348 | { 349 | "data": { 350 | "text/plain": [ 351 | "Text(0.5, 1.0, 'Discharge B0005')" 352 | ] 353 | }, 354 | "execution_count": 10, 355 | "metadata": {}, 356 | "output_type": "execute_result" 357 | }, 358 | { 359 | "data": { 360 | "image/png": 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\n", 361 | "text/plain": [ 362 | "
    " 363 | ] 364 | }, 365 | "metadata": {}, 366 | "output_type": "display_data" 367 | } 368 | ], 369 | "source": [ 370 | "\n", 371 | "%matplotlib inline\n", 372 | "import matplotlib.pyplot as plt\n", 373 | "import seaborn as sns\n", 374 | "plot_df = dataset.loc[(dataset['cycle']>=1),['cycle','Capacity']]\n", 375 | "plot_per=data_test.loc[(data_test['cycle']>=90),['cycle','pre']]\n", 376 | "sns.set_style(\"darkgrid\")\n", 377 | "plt.figure(figsize=(10, 5))\n", 378 | "plt.plot(plot_df['cycle'], plot_df['Capacity'], label=\"Actual data\", color='blue')\n", 379 | "plt.plot(plot_per['cycle'],plot_per['pre'],label=\"Prediction data\", color='red')\n", 380 | "#plt.plot(pred)\n", 381 | "#Draw threshold\n", 382 | "plt.plot([0.,168], [1.4, 1.4])\n", 383 | "plt.ylabel('Capacity')\n", 384 | "# make x-axis ticks legible\n", 385 | "adf = plt.gca().get_xaxis().get_major_formatter()\n", 386 | "plt.xlabel('cycle')\n", 387 | "plt.title('Discharge B0005')" 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": null, 393 | "metadata": {}, 394 | "outputs": [], 395 | "source": [] 396 | } 397 | ], 398 | "metadata": { 399 | "kernelspec": { 400 | "display_name": "Python 3.6 (tensorflow)", 401 | "language": "python", 402 | "name": "tensorflow" 403 | }, 404 | "language_info": { 405 | "codemirror_mode": { 406 | "name": "ipython", 407 | "version": 3 408 | }, 409 | "file_extension": ".py", 410 | "mimetype": "text/x-python", 411 | "name": "python", 412 | "nbconvert_exporter": "python", 413 | "pygments_lexer": "ipython3", 414 | "version": "3.7.1" 415 | } 416 | }, 417 | "nbformat": 4, 418 | "nbformat_minor": 2 419 | } 420 | -------------------------------------------------------------------------------- /log: -------------------------------------------------------------------------------- 1 | 2 | Key Words: 3 | ======================================== 4 | Remaining-Useful-life (RUL): 5 | 6 | Health Indicator of a battery (HI) 7 | 8 | Prognostic and Health Management (PHM) 9 | 10 | End of the battery’s service life (EOL) 11 | 12 | ========================================== 13 | Auto Regressive Moving-Average (ARMA) models 14 | 15 | Artificial Neural Networks (ANN) 16 | 17 | Support vector machine (SVM) 18 | 19 | Long short-term memory (LTSM) 20 | --------------------------------------------------------------------------------