Research on explainable lithium battery health state estimation method with multi-feature extraction
The estimation of the state of health (SOH)for lithium-ion batteries is an essential part for battery management system (BMS).It is crucial to have an accurate prediction for the SOH of lithium-ion batteries to ensure the safe and stable operation.In order to address the issues of difficult access to the capacity decline state and the lack of transparency of the"black-box"model,this paper proposes an explainable lithium-ion batteries SOH estimation method based on multi-feature extraction.Firstly,in the data processing stage,the health features are extracted by introducing a combination of direct measurement and second-order processing;then the battery SOH is estimated by the XGBoost model,and the Shapley additive explanations (SHAP)algorithm is introduced to explain the marginal contribution of each health feature to the prediction results from the local loop and global levels,respectively;finally,the effectiveness of the proposed method is verified by SOH prediction experiments on three batteries.The results of the comparative experiments indicate that the mean absolute error (MAE)and root mean square error (RMSE )of the proposed lithium-ion battery capacity prediction model are lower than 0.7％ and 1.0％,respectively.
lithium-ion batteriesstate of healthShapley additive explanationsmulti-feature extractionXGBoost
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