RUL-Prediction-for-Li-ion-Batteries With its use seen in critical areas of safety and security, it is essential for lithium-ion batteries to be reliable. Prediction of the Remaining Useful Life (RUL) can give insights into the health of the battery. Variations of Recurrent Neural Networks (RNN) are employed to learn the capacity degradation trajectories of lithium-ion batteries. Using several regressor models as the baseline, an ensemble of RNNs is created to overcome the shortcomings of one RNN over the other. The critical point approach and the data-driven approach for regressor models and neural network models respectively help predict the RUL.
Project Report
Various RNN Model Results
Experiment Model Training RMSE Testing RMSE Validation RMSE
Experiment 1 LSTM 0.0312 0.0304 0.0311 BiLSTM 0.287 0.2792 0.3259 GRU 0.0278 0.0342 0.0356 BiGRU 0.0901 0.0945 0.1059 Experiment 2 LSTM 0.019 0.0173 0.0122 BiLSTM 0.5521 0.1376 0.4871 GRU 0.0962 0.0868 0.1957 BiGRU 1.568 1.3482 1.7741 Experiment 3 LSTM 0.0183 0.0336 0.0542 BiLSTM 0.107 0.1108 0.1237 GRU 0.029 0.0425 0.0516 BiGRU 0.034 0.0454 0.053 Experiment 4 LSTM 0.0248 0.0236 0.0232 BiLSTM 0.2583 0.2232 0.1943 GRU 0.0152 0.0242 0.0452 BiGRU 0.2186 0.2282 0.1775 Experiment 5 LSTM 0.0145 0.0981 0.1329 BiLSTM 1.2602 1.1037 0.9943 GRU 0.0253 0.0811 0.1761 BiGRU 0.3437 0.4544 0.4535 Experiment 6 LSTM 0.0123 0.0189 0.0277 BiLSTM 0.7338 0.6262 0.6111 GRU 0.0967 0.1094 0.2436 BiGRU 0.138 0.2562 0.2627 Experiment 7 LSTM 0.0132 0.0253 0.0245 BiLSTM 0.2486 0.3564 0.3278 GRU 0.0578 0.0645 0.0689 BiGRU 0.1896 0.2486 0.2156 Experiment 8 LSTM 0.0226 0.0356 0.0312 BiLSTM 0.227 0.2792 0.4123 GRU 0.0156 0.0236 0.0384 BiGRU 0.0689 0.1562 0.1047 Experiment 9 LSTM 0.0196 0.0265 0.0241 BiLSTM 0.3568 0.3956 0.4256 GRU 0.0452 0.0546 0.0514 BiGRU 0.0918 0.1256 0.1298
Experiment Model Train RMSE Validation RMSE Test RMSE
Experiment 1 Experiment1_CatBoostRegressor 0.007525 0.027265 0.020191 Experiment1_ExtraTreesRegressor 2.05E-15 0.032114 0.019708 Experiment 2_LGBMRegressor 0.015742 0.01869 0.121217 Experiment 2 Experiment 2_RandomForestRegressor 0.008446 0.016777 0.118291 Experiment 3 Experiment 3_ExtraTreesRegressor 1.97E-15 0.027225 0.050309 Experiment 3_XGBRegressor 0.001102 0.032008 0.052786 Experiment 4 Experiment 4_ExtraTreeRegressor 0 0.038955 0.096077 Experiment 4_LGBMRegressor 0.064483 0.076645 0.094264 Experiment 5 Experiment 5_DecisionTreeRegressor 0 0.036995 0.027503 Experiment 5_RandomForestRegressor 0.023868 0.033271 0.033192 Experiment 6 Experiment 6_HuberRegressor 0.012126 0.018583 0.020066 Experiment 6_LinearRegression 0.01206 0.018489 0.019934 Experiment 7 Experiment 7_HuberRegressor 0.015001 0.026221 0.013487 Experiment 7_LinearRegression 0.014968 0.026394 0.013531 Experiment 7_LinearSVR 0.016352 0.029895 0.01475 Experiment 8 Experiment 8_DecisionTreeRegressor 0 0.075652 0.339136 Experiment 8_LinearSVR 0.042245 0.108166 0.342323 Experiment 9 Experiment 9_CatBoostRegressor 0.006819 0.043317 0.031779 Experiment 9_ExtraTreesRegressor 1.59E-15 0.039875 0.031513 Experiment 9_LGBMRegressor 0.04493 0.124974 0.031858
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