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RNN-flavoured Ensembling to Predict Remaining Useful Life of Lithium-ion Batteries

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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.

Report

Project Report

Results

Various RNN Model Results

ExperimentModelTraining RMSETesting RMSEValidation RMSE
Experiment 1LSTM0.03120.03040.0311
 BiLSTM0.2870.27920.3259
 GRU0.02780.03420.0356
 BiGRU0.09010.09450.1059
Experiment 2LSTM0.0190.01730.0122
 BiLSTM0.55210.13760.4871
 GRU0.09620.08680.1957
 BiGRU1.5681.34821.7741
Experiment 3LSTM0.01830.03360.0542
 BiLSTM0.1070.11080.1237
 GRU0.0290.04250.0516
 BiGRU0.0340.04540.053
Experiment 4LSTM0.02480.02360.0232
 BiLSTM0.25830.22320.1943
 GRU0.01520.02420.0452
 BiGRU0.21860.22820.1775
Experiment 5LSTM0.01450.09810.1329
 BiLSTM1.26021.10370.9943
 GRU0.02530.08110.1761
 BiGRU0.34370.45440.4535
Experiment 6LSTM0.01230.01890.0277
 BiLSTM0.73380.62620.6111
 GRU0.09670.10940.2436
 BiGRU0.1380.25620.2627
Experiment 7LSTM0.01320.02530.0245
 BiLSTM0.24860.35640.3278
 GRU0.05780.06450.0689
 BiGRU0.18960.24860.2156
Experiment 8LSTM0.02260.03560.0312
 BiLSTM0.2270.27920.4123
 GRU0.01560.02360.0384
 BiGRU0.06890.15620.1047
Experiment 9LSTM0.01960.02650.0241
 BiLSTM0.35680.39560.4256
 GRU0.04520.05460.0514
 BiGRU0.09180.12560.1298

Ensembling Results

ExperimentModelTrain RMSEValidation RMSETest RMSE
Experiment 1Experiment1_CatBoostRegressor0.0075250.0272650.020191
 Experiment1_ExtraTreesRegressor2.05E-150.0321140.019708
 Experiment 2_LGBMRegressor0.0157420.018690.121217
Experiment 2Experiment 2_RandomForestRegressor0.0084460.0167770.118291
Experiment 3Experiment 3_ExtraTreesRegressor1.97E-150.0272250.050309
 Experiment 3_XGBRegressor0.0011020.0320080.052786
Experiment 4Experiment 4_ExtraTreeRegressor00.0389550.096077
 Experiment 4_LGBMRegressor0.0644830.0766450.094264
Experiment 5Experiment 5_DecisionTreeRegressor00.0369950.027503
 Experiment 5_RandomForestRegressor0.0238680.0332710.033192
Experiment 6Experiment 6_HuberRegressor0.0121260.0185830.020066
 Experiment 6_LinearRegression0.012060.0184890.019934
Experiment 7Experiment 7_HuberRegressor0.0150010.0262210.013487
 Experiment 7_LinearRegression0.0149680.0263940.013531
 Experiment 7_LinearSVR0.0163520.0298950.01475
Experiment 8Experiment 8_DecisionTreeRegressor00.0756520.339136
 Experiment 8_LinearSVR0.0422450.1081660.342323
Experiment 9Experiment 9_CatBoostRegressor0.0068190.0433170.031779
 Experiment 9_ExtraTreesRegressor1.59E-150.0398750.031513
 Experiment 9_LGBMRegressor0.044930.1249740.031858

References

Time Series

  1. Time Series Prediction: How Is It Different From Other Machine Learning? [ML Engineer Explains]
  2. A Comprehensive Guide to Time Series Analysis
  3. A RoadMap to Time-Series Analysis

Papers

  1. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
  2. Remaining Useful Life Prediction and State of Health Diagnosis of Lithium-Ion Battery Based on Second-Order Central Difference Particle Filter
  3. Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach
  4. Novel Statistical Analysis Approach for Remaining Useful Life Prediction of Lithium-Ion Battery

RUL Prediction Code

  1. RUL Prediction for Li-ion Batteries using Critical Point
  2. Estimation of the Remaining Useful Life (RUL) of Lithium-ion batteries using Deep LSTMs.
  3. Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

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