Affiliations: School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China
Correspondence:
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Corresponding author: Guosheng Feng, School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China. E-mail: fgs2005@126.com.
Abstract: To improve the performance of the battery management system in hybrid electric vehicle (HEV), the core is to estimate the state of charge (SOC) and the state of power capability (SOP) of power battery quickly and accurately on-line. Firstly, in order to improve the SOC estimation accuracy and reduce the estimation error of battery, an improved particle filter algorithm based on particle swarm optimization (PSO) is proposed. Aiming at the uncertainty of system noise in traditional particle filter (PF) algorithm, the PSO algorithm is used to optimize the system noise of PF and to improve the estimation accuracy. Secondly, a method that regards the battery voltage, current and the optimized estimation of SOC as constraints to predict the actual maximum charge-discharge power of the battery is proposed. The simulation results show that the optimized SOC estimation and SOP prediction algorithm has higher accuracy and is applicable to the dynamic estimation of the actual driving cycles of hybrid electric vehicles.
Keywords: Power battery, SOC, SOP, particle filtering, particle swarm optimization
