Battery inconsistency evaluation based on hierarchical weight fusion and fuzzy comprehensive evaluation method

Abstract

Inconsistency is a crucial factor that affects the lithium-ion battery pack performance. Reliable cell inconsistency evaluation is essential for the efficient and safe usage of batteries. This study develops a fuzzy comprehensive evaluation (FCE) method based on hierarchical weight fusion to quantitatively evaluate the cell inconsistency. First, through an analysis of how the internal parameters change as the battery ages, the capacity, ohmic resistance, open circuit voltage and the constant current charging time are extracted as the inconsistency indicators from the collected data. They not only reflect the static and dynamic properties of batteries, but also cover the static, charging, and discharging processes. Next, a novel hierarchical weight fusion approach is proposed to achieve weight assignment by combining the entropy weight method, criteria importance through inter-criteria correlation (CRITIC) method and sequential relationship analysis (G1) method, which can incorporate the benefits of the objective and subjective weighting methods. Finally, the FCE method is utilized to quantitatively evaluate the cell inconsistency of lithium-ion batteries. Experimental results demonstrate that the proposed hierarchical weight fusion-based FCE approach can provide a clear evaluation of the degree of inconsistency across various datasets. Compared with the entropy weight-based, CRITIC-based and G1-based methods, the proposed method is more rational and comprehensive.