Ensemble Learning in Li-Ion Battery Management Systems: Focus on Voting Regression for Capacity Estimation

Accurate estimation of discharge capacity in lithium-ion batteries is very important for optimizing their performance and longevity and directly affects the efficiency of battery management systems (BMS). Traditional models frequently encounter challenges in handling the non-linearities and complex interdependencies inherent in battery behavior. This study introduces a robust voting regression-based approach that combines multiple regression models to improve prediction accuracy and reliability. We employ four different regression configurations, Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Regression and K-Nearest Neighbor (KNN) to form a combined estimator through voting mechanisms. The robustness of our voting-based approach is further validated through extensive experimentation on two real-world battery datasets including various operational conditions. Our results show that the voting regression approach we propose provides stable and accurate performance, making it a viable tool for different BMS applications. We also discuss the potential practical implications of our proposed voting approach and suggest directions for future research to further refine and adapt this approach to different types of battery technologies and configurations.