A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Abstract

Accurately estimating the state of charge (SOC) is imperative for ensuring safe and dependable battery utilization. However, accurately calculating SOC for LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 (LMFP/NCM) batteries can be challenging due to their two flat voltage platforms and significant temperature dependence. To improve estimation accuracy, a battery SOC estimation method based on a dual Kalman filter (DKF) was proposed. The adaptive unscented Kalman filter (AUKF) process starts with the introduction of Schmidt orthogonal transform, which is subsequently employed in the algorithm’s sampling point selection procedure to mitigate computational complexity. Moreover, the utilization of the multi-innovation theory serves to enhance the accuracy of algorithmic estimation. The extended Kalman filter is used to identify the parameters of the equivalent circuit model online while simultaneously carrying out battery SOC estimation. This approach mitigates the impact of variations in battery model parameters during charging and discharging processes. Under complex conditions, the algorithm’s average error is less than 0.53%, demonstrating its effectiveness in improving SOC estimation accuracy as evidenced by comparison between experiment and simulation results. It has reference significance for optimizing LMFP/NCM battery SOC estimation.