<scp>SOC</scp> estimation based on the gas‐liquid dynamics model using particle filter algorithm
Huanhuan Li, Zhiwei Qu, Tong Xu, Yaping Wang, Xiaosong Fan, Haobin Jiang, Chaochun Yuan, Long Chen
Abstract
Lithium-ion battery state of charge (SOC) estimation is the basis for other state estimations in electric vehicle (EV) battery management systems. Accurate SOC estimation can prevent overcharge and discharge of the battery, improve discharge efficiency and prolong cycle life. A high-precision SOC estimation method based on the gas-liquid dynamics (GLD) model is presented. The standard particle filter (PF) and the unscented particle filter (UPF) algorithm are successively used for optimizing and improving the chattering problem of the original GLD model. The results show that the two kinds of PF algorithms can effectively solve the chattering problem of the GLD model and improve the estimation accuracy. Among them, UPF has higher accuracy and the maximum estimation error is less than 2%. The PF method for estimating SOC introduced in this paper has the characteristics of high precision and large amount of calculation. Due to the above characteristics, this method can be used as a cloud SOC calculation method to periodically correct the local SOC of the vehicle.