Improved LightGBM-Based Framework for Electric Vehicle Lithium-Ion Battery Remaining Useful Life Prediction Using Multi Health Indicators

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To improve the prediction accuracy and prediction speed of battery remaining useful life (RUL), this paper proposes an improved light gradient boosting machine (LightGBM)-based framework. Firstly, the features from the electrochemical impedance spectroscopy (EIS) and incremental capacity-differential voltage (IC-DV) curve are extracted, and the open circuit voltage and temperature are measured; then, those are regarded as multi HIs to improve the prediction accuracy. Secondly, to adaptively adjust to multi HIs and improve prediction speed, the loss function of the LightGBM model is improved by the adaptive loss. The adaptive loss is utilized to adjust the loss function form and limit the saturation value for the first-order derivative of the loss function so that the improved LightGBM can achieve an adaptive adjustment to multiple HIs (ohmic resistance, charge transfer resistance, solid electrolyte interface (SEI) film resistance, Warburg resistance, loss of conductivity, loss of active material, loss of lithium ion, isobaric voltage drop time, and surface average temperature) and limit the impact of error on the gradient. The model parameters are optimized by the hyperparameter optimization method, which can avoid the lower training efficiency caused by manual parameter adjustment and obtain the optimal prediction performance. Finally, the proposed framework is validated by the database from the battery aging and performance testing experimental system. Compared with traditional prediction methods, GBDT (1.893%, 4.324 s), 1D-CNN (1.308%, 47.381 s), SVR (1.510%, 80.333 s), RF (1.476%, 852.075 s), and XGBoost (1.119%, 24.912 s), the RMSE and prediction time of the proposed framework are 1.078% and 15.728 s under the total HIs. The performance of the proposed framework under a different number of HIs is also analyzed. The experimental results show that the proposed framework can achieve the optimal prediction accuracy (98.978%) under the HIs of resistances, loss modes, and isobaric voltage drop time.