State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method

Abstract

The voltage plateau phase of LiFeO4 (LFP) batteries presents a formidable challenge when estimating the state of charge (SOC). Although numerous data-driven methods have been developed to address this issue, they often depend heavily on high-quality training data. Furthermore, these methods are typically regarded as “black-box” models, lacking interpretability. To overcome these challenges, a novel approach that integrates both model-based and data-driven techniques for battery SOC estimation is proposed in this paper. The proposed method is grounded in the battery model and complemented by the data-driven model, thus enhancing interpretability by incorporating domain knowledge. To reduce computational complexity, the Rint model is used for rough SOC estimation, with the eXtreme Gradient Boosting model (XGBoost) used for residual learning. The parameters of the battery model serve as input features for the XGBoost model. The effectiveness of the proposed method is empirically validated under different dynamic testing profiles. Experimental results demonstrate that integration battery domain knowledge into data-driven approaches not only enhances method interpretability but also significantly improves the SOC estimation accuracy for LFP batteries. Moreover, it exhibits robustness and exceptional generalization performance under unseen dynamic conditions, yielding root mean square errors of less than 1 % and maximum errors of less than 2 %.