A Health Assessment Method for Lithium-Ion Batteries Based on Evidence Reasoning Rules with Dynamic Reference Values

The health assessment of lithium-ion batteries holds great research significance in various areas such as battery management systems, battery usage and maintenance, and battery economic evaluation. However, because environmental perturbations are not taken into account during the assessment, the accuracy and reliability of the assessment are limited. Thus, a health assessment model for lithium-ion batteries based on evidence reasoning rules with dynamic reference value (ER-DRV) is proposed in this paper. Firstly, considering that the data are subject to changes, dynamic reference values, real-time weights, and real-time reliability were utilized in the model to ensure the effectiveness and accuracy of the assessment. Moreover, an enhanced optimization method based on the whale optimization algorithm (WOA) was developed to improve the accuracy of the assessment model. In addition, the robustness of the ER-DRV model was studied with perturbation analysis methods. Finally, the proposed method was validated on two open lithium-ion battery datasets. The experimental results show that the health assessment method proposed in this article not only has higher accuracy and transparent reasoning process but also has strong robustness and good generalization ability.