Abstract In order to improve the accuracy of estimation of residual charge of lithium battery, a method of estimation of SOC of lithium battery based on online parameter identification and improved particle filter algorithm is proposed. Aiming at the problem of particle degradation in particle filtering, gray wolf optimization is introduced to optimize particle distribution with its strong global optimization ability to ensure particle diversity, effectively suppress particle degradation, and improve the filtering accuracy. The recursive least square method with forgetting factor is used to update the model parameters in real-time and run alternately with the improved particle filter algorithm to further improve the estimation accuracy of SOC. The experimental results show that the average estimation error of the improved algorithm is always less than ±0.15%. Compared with the extended Kalman filter and unscented Kalman filter, the improved algorithm has higher estimation accuracy and stability in the estimation of battery SOC.
WU Zhong-qiang,HU Xiao-yu,MA Bo-yan, et al. SOC Estimation of Lithium Batteries Based on RFF and GWO-PF[J]. Acta Metrologica Sinica, 2022, 43(9): 1200-1207.
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2022, Vol. 43 
