An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

Abstract

The state of charge (SOC) estimation of lithium-ion battery is a crucial portion of the battery management system (BMS). The high-precision estimation is the foundation of BMS safety and efficiency. To that extent, a fractional-order algorithm with time-varying parameters model is proposed to ensure the accuracy of the SOC. Since the battery state changes slowly and is related to the state in the past, this study proposes a memory factor M containing the battery state in the past to estimate the SOC. Moreover, by comparing the experimental results of different orders, the most appropriate fractional order is determined. In order to eliminate the influence of noises introduced into historical data processing, an adaptive noise factor is added to the algorithm. The experimental results confirm that the maximum error of the adaptive fractional-order extended Kalman (AFEKF) estimation is less than 2%, which indicates that the estimation method provides a higher accuracy than the extended Kalman filter.