LightGBM-Based Framework for Lithium-Ion Battery Remaining Useful Life Prediction Under Driving Conditions

Abstract

The remaining useful life (RUL) degradation under driving conditions is complex. The features from incremental capacity-differential voltage (IC-DV) curves and electrochemical impedance spectroscopy (EIS) can be implemented to identify the battery degradation modes and predict RUL. This paper proposes a light gradient boosting machine (LightGBM) based framework with electro- chemical theory to achieve RUL prediction under driving conditions. The degradation modes are identified as loss of conductivity, loss of active material and loss of lithium ion, and the EIS is refined as ohmic resistance, charge transfer resistance, solid electrolyte interphase film resistance, and Warburg resistance. The LightGBM model is improved by the adaptive robust loss function to achieve multi-loss functions adaptive adjustment for different cases and limit the effect of measurement noise on gradients. Based on the perspective of multitask learning, the Pearson correlation is analyzed to design the sharing principle, which ensures full use of features and reduce experimental costs. The proposed framework is validated by the database under four vibration cases (static, X-axis, Y-axis, and Z-axis). Experimental results demonstrate that the proposed framework is capable of providing accurate and steady RUL prediction under driving conditions even with measurement noise. The proposed framework could guide periodic maintenance and stable operation to avoid risks.