Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model

Abstract

The lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation of electric vehicles and the sustainable development of green transportation. We collected multiple sets of charge–discharge cycle experimental data for LiFePO4 LIB and employed several traditional machine learning models to predict the SOH of LIBs. It was found that the RF model yielded relatively superior predictive results, confirming the feasibility of applying the RF model to SOH prediction for the electric vehicle LIB. Building upon this foundation, further research was conducted on the RF improved model for LIB SOH prediction. The PSO algorithm was employed to adaptively optimize five major parameters of the RF model: max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. This adaptation addresses the issue of prediction errors that stem from human experience to optimize parameters in the RF model. The results indicate that the RF improved model proposed in this paper can further improve the prediction accuracy of LIB SOH. Its model evaluation index also outperform others, demonstrating the effectiveness of this approach in the management of LIB SOH for new-energy electric vehicles. This contributes significantly to urban environmental protection and the development of green transportation.

Keywords

• new-energy electric vehicles
• lithium-ion battery
• state-of-health prediction
• random forest model
• particle swarm optimization