├── Figures
    ├── Ah_utility.pdf
    ├── MTBC_utility.pdf
    ├── pf_rul_prediction.pdf
    ├── utility_panel_cell27.pdf
    ├── all_124_capacity_fade_curves.pdf
    └── many_pf_trajectory_predictions.pdf
├── 124 LFP Cell Conditions.xlsx
├── capacity_dataset_124_lfp.pkl
├── LICENSE
├── README.md
├── create_capacity_dataset_from_raw_files.py
└── 124 LFP Capacity Data
    ├── test1
        ├── cell22.csv
        ├── cell41.csv
        ├── cell39.csv
        ├── cell40.csv
        ├── cell23.csv
        ├── cell37.csv
        ├── cell25.csv
        ├── cell31.csv
        └── cell30.csv
    └── train
        ├── cell21.csv
        ├── cell39.csv
        ├── cell22.csv
        ├── cell23.csv
        ├── cell28.csv
        ├── cell38.csv
        ├── cell36.csv
        ├── cell40.csv
        ├── cell31.csv
        ├── cell32.csv
        └── cell37.csv


/Figures/Ah_utility.pdf:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/Figures/Ah_utility.pdf


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/Figures/MTBC_utility.pdf:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/Figures/MTBC_utility.pdf


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/124 LFP Cell Conditions.xlsx:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/124 LFP Cell Conditions.xlsx


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/Figures/pf_rul_prediction.pdf:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/Figures/pf_rul_prediction.pdf


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/capacity_dataset_124_lfp.pkl:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/capacity_dataset_124_lfp.pkl


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/Figures/utility_panel_cell27.pdf:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/Figures/utility_panel_cell27.pdf


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/Figures/all_124_capacity_fade_curves.pdf:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/Figures/all_124_capacity_fade_curves.pdf


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/Figures/many_pf_trajectory_predictions.pdf:
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https://raw.githubusercontent.com/acthelen/battery_digital_twin/HEAD/Figures/many_pf_trajectory_predictions.pdf


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 Adam Thelen
 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 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | # battery_digital_twin
 2 | 
 3 | Questions regarding this repository should be directed to Adam Thelen (adamcthelen@gmail.com).
 4 | 
 5 | $\ $
 6 | 
 7 | This repository contains the scripts and preprocessed data to recreate the figures and results presented in the paper:
 8 | 
 9 | **A Comprehensive Review of Digital Twin - Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives**
10 |   
11 | Adam Thelen<sup>1</sup>, Xiaoge Zhang<sup>2</sup>, Olga Fink<sup>3</sup>, Yan Lu<sup>4</sup>, Sayan Ghosh<sup>5</sup>, Byeng D.
12 | Youn<sup>6</sup>, Michael D. Todd<sup>7</sup>, Sankaran Mahadevan<sup>8</sup>, Chao Hu<sup>1*</sup> and Zhen Hu<sup>9*</sup>
13 | 
14 | 1. Department of Mechanical Engineering, Iowa State University, Ames, 50011, IA, USA.
15 | 2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
16 | 3. Intelligent Maintenance and Operations Systems, Swiss Federal Institute of Technology Lausanne, Lausanne, 12309, NY, Switzerland.
17 | 4. Information Modeling and Testing Group, National Institute of Standards and Technology, Gaithersburg, 20877, MD, USA.
18 | 5. Probabilistic Design and Optimization group, GE Research, Niskayuna, 12309, NY, USA.
19 | 6. Department of Mechanical Engineering, Seoul National University, Gwanak-gu, 151-742, Seoul, Republic of Korea.
20 | 7. Department of Structural Engineering, University of California, San Diego, La Jolla, 92093, CA, USA.
21 | 8. Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, 37235, TN, USA.
22 | 9. Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, Dearborn, 48128, MI, USA.
23 | 
24 | $\*$ Corresponding author(s). E-mail(s): chaohu@iastate.edu; zhennhu@umich.edu;
25 | 
26 | Contributing authors: acthelen@iastate.edu; xiaoge.zhang@polyu.edu.hk; olga.fink@epfl.ch; yan.lu@nist.gov; sayan.ghosh1@ge.com; bdyoun@snu.ac.kr; mdtodd@eng.ucsd.edu; sankaran.mahadevan@vanderbilt.edu
27 | 
28 | ## NOTE
29 | 
30 | The dataset is labeled as having 124 cells, however, the authors of the orignal dataset noted that one of the cells degraded extremely quickly, in roughly 150 cycles. We did not include this cell in the study. Test - 2017-06-30_1C-4per_6C_CH9. EL150800460605. Primary Test Dataset. ~148 cycles. 
31 | 
32 | 
33 | ## Scripts Overview
34 | 
35 | The script `create_capacity_dataset_from_raw_files.py` reads the files in the folder `124 LFP Capacity Data` and compiles them into an easy to use dataset which is then saved as `capacity_dataset_124_lfp.pkl`. 
36 | 
37 | The particle filter code is presented in two different files. The first file, `plot_pf_projection_each_cycle_single_cell.py` is used to run the particle filter on a single cell. This scrip is best used for understanding how the particle filter works, as it shows how the particle filter prediction evolves over time as more capacity information is available. 
38 | 
39 | The second file, `run_pf_and_optimize_retirement_six_cells.py` runs the particle filter code for the six randomly selected cells shown in the paper, and optimizes the retirement of each cell individually. This script contains the multi-attribute utility theory (MAUT) algorithm described in the paper. This script is best used for understanding how the particle filter and the MAUT algorithm interact, and how the optimal retirement changes on a cell-by-cell basis. 
40 | 
41 | 
42 | 


--------------------------------------------------------------------------------
/create_capacity_dataset_from_raw_files.py:
--------------------------------------------------------------------------------
  1 | import pickle
  2 | import numpy as np
  3 | import matplotlib.pyplot as plt
  4 | import pandas as pd
  5 | import matplotlib
  6 | 
  7 | # load in the conditions of each cell and seperate them
  8 | conditions = pd.read_excel('124 LFP Cell Conditions.xlsx')
  9 | train_conds = conditions[ conditions['Dataset'] == 'Train']
 10 | test1_conds = conditions[ conditions['Dataset'] == 'Prim. Test']
 11 | test2_conds = conditions[ conditions['Dataset'] == 'Sec. test']
 12 | 
 13 | # The original authors of the dataset noticed that one of the cells in the primary
 14 | # test dataset had very short lifetime. The authors removed the cell from the 
 15 | # dataset before performing any analysis. We have done the same. This is why
 16 | # the dataset only has 123 cells in total when it is labeled "124." 
 17 | test1_conds = test1_conds.drop(index=42) # Was previously 44 which was incorrect. 
 18 | 
 19 | # Load in the data
 20 | x_train = []
 21 | y_train = []
 22 | for i in range(0,41):
 23 |     data = np.loadtxt(r'124 LFP Capacity Data\train\cell{}.csv'.format(i+1), 
 24 |                       delimiter=',')
 25 |     x_train.append(data[:,0])
 26 |     y_train.append(data[:,1])
 27 | 
 28 | x_test1 = []
 29 | y_test1 = []
 30 | for i in range(0,42):
 31 |     data = np.loadtxt(r'124 LFP Capacity Data\test1\cell{}.csv'.format(i+1), 
 32 |                       delimiter=',')
 33 |     x_test1.append(data[:,0])
 34 |     y_test1.append(data[:,1])
 35 | 
 36 | 
 37 | x_test2 = []
 38 | y_test2 = []
 39 | for i in range(0,40):
 40 |     data = np.loadtxt(r'124 LFP Capacity Data\test2\cell{}.csv'.format(i+1), 
 41 |                       delimiter=',')
 42 |     x_test2.append(data[:,0])
 43 |     y_test2.append(data[:,1])
 44 | 
 45 | 
 46 | sz = []
 47 | for i in range(0,len(x_train)):
 48 |     sz.append(len(x_train[i]))
 49 | sz = np.vstack(sz)
 50 | sz_df = pd.DataFrame(sz)
 51 | 
 52 | 
 53 | # Reset the index before saving
 54 | test1_conds = test1_conds.reset_index().drop(columns='index')
 55 | test2_conds = test2_conds.reset_index().drop(columns='index')
 56 | train_conds = train_conds.reset_index().drop(columns='index')
 57 | 
 58 | 
 59 | # Combine data for plotting
 60 | all_x = x_train + x_test1 + x_test2
 61 | all_y = y_train + y_test1 + y_test2
 62 | 
 63 | 
 64 | 
 65 | 
 66 | 
 67 | ###############################################################################
 68 | 
 69 | 
 70 | ## Extrapolate the capacity fade curves ##
 71 | 
 72 | from scipy.optimize import curve_fit
 73 | 
 74 | def linear_model(x, a, b):
 75 |     return a*x + b
 76 | 
 77 | extrapolated_cap_data_all = []
 78 | extrapolated_cycle_data_all = []
 79 | # Perform extrapolation on the capacity data so the cells last longer. Typically,
 80 | # a much lower EOL threshold is used for grid storage.
 81 | for i in range(0, len(all_x)):
 82 |     data = all_y[i]
 83 |     x = np.arange(1,len(data)+1,1)
 84 |     x = x[-30:]
 85 |     y = data[-30:]
 86 |     
 87 |     extrap_len = 3000
 88 |     
 89 |     parameters, cov = curve_fit(linear_model, x, y)
 90 |     a, b, = parameters
 91 |     next_x = np.linspace(x[-1:] + 1, x[-1:]+extrap_len, extrap_len)
 92 |     next_y = linear_model(next_x, a, b)
 93 |     # idxs = np.where(next_y > 0.790)[0]
 94 |     # next_y = next_y[idxs]
 95 |     
 96 |     ext_data = np.concatenate((data.reshape((-1,1)), next_y), axis=0).reshape(-1)
 97 |     extrapolated_cap_data_all.append(ext_data)
 98 |     extrapolated_cycle_data_all.append(np.arange(2,len(ext_data)+2,1))
 99 | 
100 | # Regroup the data
101 | x_train_ext = extrapolated_cycle_data_all[0:0+41]
102 | x_test1_ext = extrapolated_cycle_data_all[41:41+42]
103 | x_test2_ext = extrapolated_cycle_data_all[41+42:41+42+40]
104 | 
105 | y_train_ext = extrapolated_cap_data_all[0:0+41]
106 | y_test1_ext = extrapolated_cap_data_all[41:41+42]
107 | y_test2_ext = extrapolated_cap_data_all[41+42:41+42+40]
108 | 
109 | 
110 | # Create the save dictionary
111 | my_dic = {'x_train':x_train_ext,
112 |           'x_test1':x_test1_ext,
113 |           'x_test2':x_test2_ext,
114 |           
115 |           'y_train':y_train_ext,
116 |           'y_test1':y_test1_ext,
117 |           'y_test2':y_test2_ext,
118 |           
119 |           'train_conds':train_conds,
120 |           'test1_conds':test1_conds,
121 |           'test2_conds':test2_conds,
122 |     }
123 | 
124 | output = open(r'capacity_dataset_124_lfp.pkl', 'wb')
125 | pickle.dump(my_dic, output)
126 | output.close()
127 | 
128 | 
129 | 
130 | # Combine data for plotting
131 | all_x = x_train_ext + x_test1_ext + x_test2_ext
132 | all_y = y_train_ext + y_test1_ext + y_test2_ext
133 | 
134 | 
135 | 
136 | 
137 | ###############################################################################
138 | 
139 | 
140 | 
141 | 
142 | # Plotting preferences
143 | plt.rcParams["font.family"] = "Times New Roman"
144 | plt.rcParams["mathtext.default"] = "rm"
145 | plt.rcParams["font.family"] = "Times New Roman"
146 | plt.rcParams['mathtext.fontset'] = 'cm'
147 | plt.rcParams['mathtext.rm'] = 'Times New Roman'
148 | 
149 | 
150 | # Determine lifetime of each cell
151 | lifetimes = []
152 | for i in range(0, len(all_x)):
153 |     try:
154 |         lifetimes.append(all_x[i][np.where(all_y[i] < 0.88)[0][0]])
155 |     except:
156 |         lifetimes.append(all_x[i][-1])
157 | lifetimes = np.vstack(lifetimes)
158 | 
159 | # Sort the cells by cycle life
160 | sort_idxs = np.argsort(lifetimes, axis=0).reshape(-1)
161 | # sort_idxs = np.flip(sort_idxs)
162 | 
163 | # Create the colors for the lines
164 | xs = np.linspace(0,1,len(lifetimes)+30)
165 | xs = xs[30:]
166 | color_list = matplotlib.cm.Blues(xs)
167 | 
168 | 
169 | # Plot the data
170 | plt.figure(num=1, dpi=400, figsize=(4,3.5))
171 | fontsize=12
172 | c = 0
173 | for idx in sort_idxs:
174 |     plt.plot(all_x[idx], all_y[idx], color=color_list[c], linewidth=1)
175 |     c += 1
176 | plt.hlines(0.88,0,2500, colors='k', linestyles='-.')
177 | plt.xlim([0,2500])
178 | plt.ylim([0.4, 1.15])
179 | # plt.title('124 LFP Cells', fontsize=fontsize)
180 | plt.xlabel('Cycle Number', fontsize=fontsize)
181 | plt.ylabel('Discharge Capacity (Ah)', fontsize=fontsize)
182 | plt.xticks(fontsize=fontsize)
183 | plt.yticks(fontsize=fontsize)
184 | plt.savefig(r'Figures\all_124_capacity_fade_curves.pdf', bbox_inches = "tight")
185 | plt.show()
186 | 
187 | 
188 | 
189 | 
190 | 
191 | 
192 | 
193 | 
194 | 
195 | 


--------------------------------------------------------------------------------
/124 LFP Capacity Data/test1/cell22.csv:
--------------------------------------------------------------------------------
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 90 | 91,1.05,1.0669
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100 | 101,1.0464,1.0669
101 | 102,1.0454,1.0669
102 | 103,1.0452,1.0669
103 | 104,1.045,1.0669
104 | 105,1.0442,1.0669
105 | 106,1.044,1.0669
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/124 LFP Capacity Data/train/cell21.csv:
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/124 LFP Capacity Data/test1/cell41.csv:
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449 | 


--------------------------------------------------------------------------------
/124 LFP Capacity Data/train/cell39.csv:
--------------------------------------------------------------------------------
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452 | 


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/124 LFP Capacity Data/train/cell22.csv:
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464 | 


--------------------------------------------------------------------------------
/124 LFP Capacity Data/test1/cell39.csv:
--------------------------------------------------------------------------------
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492 | 


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/124 LFP Capacity Data/train/cell23.csv:
--------------------------------------------------------------------------------
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471 | 


--------------------------------------------------------------------------------
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489 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/124 LFP Capacity Data/train/cell38.csv:
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480 | 


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/124 LFP Capacity Data/train/cell36.csv:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/124 LFP Capacity Data/test1/cell23.csv:
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507 | 


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/124 LFP Capacity Data/train/cell40.csv:
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487 | 


--------------------------------------------------------------------------------
/124 LFP Capacity Data/test1/cell37.csv:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/124 LFP Capacity Data/train/cell31.csv:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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495 | 


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/124 LFP Capacity Data/test1/cell31.csv:
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--------------------------------------------------------------------------------
/124 LFP Capacity Data/train/cell37.csv:
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502 | 


--------------------------------------------------------------------------------
/124 LFP Capacity Data/test1/cell30.csv:
--------------------------------------------------------------------------------
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 52 | 53,1.0723,1.068
 53 | 54,1.0727,1.068
 54 | 55,1.0726,1.068
 55 | 56,1.0727,1.068
 56 | 57,1.0723,1.068
 57 | 58,1.0718,1.068
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 59 | 60,1.0724,1.068
 60 | 61,1.0724,1.068
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 63 | 64,1.072,1.068
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 65 | 66,1.0726,1.068
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 67 | 68,1.0726,1.068
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 70 | 71,1.0724,1.068
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 78 | 79,1.0719,1.068
 79 | 80,1.0718,1.068
 80 | 81,1.0719,1.068
 81 | 82,1.0721,1.068
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 83 | 84,1.0714,1.068
 84 | 85,1.0722,1.068
 85 | 86,1.0721,1.068
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 90 | 91,1.0717,1.068
 91 | 92,1.0717,1.068
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 93 | 94,1.0715,1.068
 94 | 95,1.0714,1.068
 95 | 96,1.0713,1.068
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 98 | 99,1.0713,1.068
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524 | 


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