SOC Estimation Method of Lithium-Ion Battery Based on Multi-innovation Adaptive Robust Untraced Kalman Filter Algorithm

In order to reduce the calculation amount of battery management system (BMS) and deal with the SOC estimation error caused by various noises, a multi-time scale estimation method for lithium-ion battery SOC was proposed based on the combination of multi-innovation adaptive robust untracked Kalman filter (MIARUKF) algorithm and untracked Kalman filter (UKF) algorithm. Based on the second-order RC equivalent circuit model, UKF algorithm is used to estimate the model parameters online on the macro time scale. MIARUKF algorithm estimates battery SOC on micro time scale. The algorithm uses multi-innovation vector to correct state estimate and timely updates noise covariance to improve the SOC estimation accuracy. H$\infty$ filtering algorithm is introduced to improve the robustness of the algorithm. Simulation validation is conducted under the UDDS working condition. The maximum absolute error of SOC estimation is 0.75%, and the average absolute error is 0.37%. Compared with UKF algorithm, the proposed algorithm has higher accuracy and stronger robustness.