SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation.