A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries

Abstract

Battery state-of-health (SOH) estimation is a critical concern of the battery management system, which significantly affects the safe and stable operation of electric vehicles. The existing SOH estimation methods mainly focus on a single model with single-source features. Hence, the generalizability of these methods is limited. In this paper, a multi-feature-based multi-model fusion method is proposed for the SOH estimation of lithium-ion batteries. Firstly, the key factors of the battery aging process are analyzed from multiple sources such as voltage, temperature, and incremental capacity curves. Seven health factors are extracted as first-level input. Secondly, the preliminary SOH predictions are generated by using multiple linear regression, support vector regression, and Gaussian process regression models, respectively. Finally, a random forest model is used to fuse the preliminary SOH predictions. To improve the model prediction accuracy, the proposed model extracts features from different sources to fully describe the battery aging process. Inspired by the advantages of multi-model fusion, the random forest regressor method is applied for fusing the multi-model. To verify the effectiveness of the proposed model, comparative experiments are carried on the Oxford battery degradation dataset. Comparing with single feature or single model estimation methods, the results demonstrate the proposed method has better accuracy and stronger robustness in SOH estimation. • A novel SOH estimation method is trained with a two-layer structure. • Seven health factors of batteries are extracted from multiple sources. • The preliminary SOH predictions are generated by using MLR, SVR, and GPR. • A random forest model is used to fuse the preliminary SOH predictions.