Lithium-ion battery health assessment method based on belief rule base with interpretability

Abstract

Health assessment is important for the safe and reliable operation of lithium-ion battery. However, there exist two typical issues in existing health assessment models for lithium-ion battery: uncertainty of the chemical reaction inside the battery and lack of interpretability of the evaluation results. The belief rule base (BRB) is a rule-based modelling approach and can deal with uncertain information in health state assessment. However, the interpretability of the lithium-ion battery health state assessment model based on BRB needs to be preserved effectively. Thus, a new lithium-ion battery health state assessment model based on belief rule base with interpretability (BRB-I) is proposed in this paper. In the BRB-I model, both structural interpretability and optimization interpretability are constructed through the mechanism analysis of lithium-ion batteries. Moreover, a new interpretability evaluation criterion is defined as the objective function to achieve a balanced optimization between interpretability and accuracy. In addition, an improved optimization method based on the whale optimization algorithm (WOA) is proposed to enhance the interpretability of the model and the optimization process. A case study on the health state assessment of B0006 lithium-ion battery is conducted to illustrate the effectiveness of the proposed method.