State of charge estimation of lithium batteries in wide temperature range based on MSIABC-AEKF algorithm

The key to a Battery Management System (BMS) is the accurate and real-time prediction of the State of Charge (SOC) of the power battery. Currently, there is relatively little research on the construction methods of battery models within a wide temperature range. A second-order RC equivalent circuit is selected to establish an Improved Equivalent Circuit Model (IECM) based on temperature compensation. The identification of IECM parameters is completed by using the multi strategy improvement of Artificial Bee Colony (MSIABC) algorithm combining with pulse discharge experimental data under different temperature conditions (-20 °C to 60 °C). Based on the experimental data of the UDDS condition and the hybrid dynamic condition under low temperature, high temperature, and time-varying temperature environments, the battery SOC is estimated by combining the IECM and Adaptive Extended Kalman Filtering (AEKF) algorithm. The experimental results show that compared with the conventional ECM-AEKF estimation method, IECM-AEKF has higher SOC estimation accuracy and environmental temperature adaptability.