A New Interpretable State-of-Health Assessment Model for Lithium-Ion Batteries With Multidimensional Adaptability Optimization

Abstract

The state-of-health (SOH) assessment of lithium-ion batteries is critical to the development and optimization of maintenance strategies. To ensure the accuracy of the assessment results, it must not only address a variety of uncertainties but also rationalize and transparently conduct the assessment process, as well as make the results interpretable and traceable. These requirements are necessary to ensure that the battery operates safely and steadily. As an interpretable modeling method, belief rule base (BRB) has been widely used in lithium-ion battery SOH assessment. However, current BRB-based models face two problems: (1) The initial reference values provided by experts often have limited accuracy due to complex internal chemistry. (2) The multidimensionality of the parameters in the interpretable optimization process and the differences in their properties should be fully considered. Therefore, this paper proposes a new SOH assessment model for lithium-ion batteries based on an interpretable BRB with multidimensional adaptability optimization (IBRB-mao). First, an interpretable knowledge and data dual-driven reference value generation method is proposed to address the issue of imprecise reference values. Expert knowledge is maintained when generating reference values using this method. Second, two interpretable multidimensional constraint strategies are proposed to ensure interpretability in the optimization process. Finally, the NASA lithium-ion battery data set is taken as a case study to validate the effectiveness of the proposed method.