Ensemble Method With Heterogeneous Models for Battery State-of-Health Estimation

Abstract

Accurate and reliable state-of-health (SOH) estimation is an important topic in battery management. Single data-driven model based SOH estimation suffers significant discrepancy problems over different cases. Moreover, existing ensemble based SOH estimation methods suffer serious problems, such as insufficient diversity of base models, complicated weight calculation, and severe overfitting. To address these problems, a stacking-based ensemble learning method for SOH estimation is proposed in this paper. A second-level learner is used to integrate three heterogeneous base models without any weight calculation step. Fused datasets are generated by cross-validation, maximizing the model generalization. Comprehensive validations are performed on batteries with two different cathode materials using two training strategies. The results show that the proposed ensemble method outperforms not only all base models (29% better than the optimal base model), but also the average method (more than 32%) and the state-of-the-art ensemble method (more than 44%).