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

Precise estimation of lithium-ion batteries's state of health (SOH) is crucial for their safe and stable operation. The existing SOH estimation methods mainly focus on a single model with multi-features. Hence, the potential of each feature cannot be fully exploited. To improve the accuracy of prediction model, a feature reuse(FR) based multi-model fusion method is proposed in this paper. Firstly, four features are extracted by analyzing the battery aging process from multiple perspectives such as sample entropy and time(micro/macro), particularly a new feature is proposed to reflect changes in battery energy. The four health factors are as first-level input and second-level feature reuse. Secondly, the preliminary SOH predictions are generated respectively by using k-nearest neighbor regression, random forest regression, linear regression, and extremely randomized tree regression models with different feature inputs. Finally, the initial SOH predictions were fused with the input features from the first stage using a Bayesian linear regression algorithm. Inspired by the advantages of feature reuse method and multi-model fusion, the feature reuse method is applied for fusing Bayesian regression multi -model. To verify the effectiveness of the proposed model, comparative experiments are carried on the Oxford and CALCE battery degradation dataset. Comparing with the single model estimation and methods without feature reuse, the proposed method has better accuracy and stronger robustness in SOH estimation.