Online State of Health Estimation for Series-Connected LiFePO₄ Battery Pack Based on Differential Voltage and Inconsistency Analysis
Zhongkai Zhou, Bin Duan, Yongzhe Kang, Qi Zhang, Yunlong Shang, Chenghui Zhang
Abstract
Low-complexity and accurate state of health (SoH) estimation of series-connected batteries has always been a difficult problem to solve in a well-designed battery management system (BMS). Lithium iron phosphate (LiFePO4) battery has been widely used as an energy storage carrier due to its better safety and longer cycle life. In this article, we proposed an online SoH estimation method for LiFePO4 battery pack based on differential voltage (DV) and inconsistency analysis. According to the aging mechanism of LiFePO4 battery, the region capacity in DV curve is extracted as a health feature to establish an aging model. Furthermore, all four inconsistency cases that affect pack capacity are analyzed, and the calculation formulas are concluded by relying only on three representative battery modules, thereby, reducing the computational burden. The pack capacity is estimated to verify the feasibility of the proposed method at different aging cycles. The estimation errors of the SoH of eight in-pack battery modules are less than 2%, and further, the maximum error for the battery pack is only 2.27% in all aging cycles. In addition, the maximum error of the SoH for the other battery pack is only slightly over 1% at different inconsistencies.