Study on SOC Estimation of Li-ion Battery Based on the Comparison of UKF Algorithm and AUKF Algorithm

Abstract

Abstract In this study, a state of charge (SOC) estimate technique for lithium-ion batteries is presented using an adaptive traceless Kalman filter (AUKF). First, the battery’s second-order RC equivalent circuit model is created, and its parameters are identified. Next, in contrast to the traceless Kalman filter (UKF) algorithm, which ignores the time-varying characteristics of the system noise when estimating the lithium-ion battery’s state of charge, the AUKF-based SOC estimation method is formed from the perspective of adaptive noise adjustment (SOC). The results of testing the AUKF algorithm in real-world settings demonstrate that it has great estimate accuracy and stability and that its estimation outcomes outperform those of the UKF method.