SOC estimation and parameter identification of battery model based on multi-innovation theory

Accurate lithium-ion battery state of charge（SOC） estimation is of great significance to ensure the safe and stable operation of battery system（BMS）. In this paper, the electrical characteristics of the ternary lithium-ion battery are tested, and based on this, we established the first-order RC equivalent circuit model for lithium-ion battery. Then we adapted the recursive least squares method with a forgetting factor which introduces the multi-innovation identification theory to identify the battery model parameters online and combine with weighted multi-innovation Kalman filter to estimate the SOC of the battery. The algorithm is verified under FUDS condition.The results show that SOC estimation accuracy and stability of the FFMILS-MIEKF algorithm are improved to varying degrees compared with the traditional EKF and UKF algorithms. Moreover, the algorithm can converge quickly under different initial SOC values, which means good robustness. As a result, the proposed SOC estimation algorithm in this paper can be well applied to actual BMS.