ERIME-UPF and CSVSF-VBL Fusion for Accurate State of Charge Inconsistency Tracking in Dynamic Battery Environments

Accurate online tracking of state of charge (SOC) inconsistency in lithium-ion battery packs is essential for safety. It is equally critical for effective battery management in real-world operation. To achieve robust performance in dynamic battery environments characterized by temperature fluctuations and cell aging, a method combining enhanced Rime optimized-unscented particle filter (ERIME-UPF) with cubature smooth variable structure filter-varying boundary layer (CSVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the hysteresis effect dynamic migration model, and the Rint model. First, module SOC is estimated using an ERIME-UPF, which adaptively adjusts the noise covariances of UPF via the enhanced RIME optimizer. Simultaneously, CSVSF-VBL employs the Rint model to estimate cell SOC inconsistencies, incorporating capacity and internal resistance coefficients into the second-order performance chattering to better capture cell inconsistency. Experiments focus on LiFePO4 batteries under various inconsistencies, temperature, and aging states. The results show that ERIME-UPF achieves an average mean absolute error (MAE) of 0.33% for module SOC estimation, while CSVSF-VBL achieves a peak MAE of 3.28% for cell SOC estimation. Demonstrating superior accuracy and robustness in tracking SOC inconsistency under dynamic and degraded operating conditions.