State of Charge Estimation for Electric Vehicle Batteries Based on a Particle Filter Algorithm

Jun BI, Yong-xing WANG, Xiao-mei ZHAO

Abstract


Accurate State of Charge (SOC) estimation is critical for improving the battery performance. In order to realize the accurate estimation of SOC for electric vehicle (EV) batteries, considering the complex operating mode and nonlinear characteristics of EV batteries, this paper proposes a particle filter algorithm for estimating SOC based on the battery data from EVs operating in Beijing. To determine the state-space model for EV batteries, the data are used to estimate the parameters of the model. Moreover, based on the actual collected data, the experiments are designed to demonstrate the particle filter algorithm. The results indicate that the estimation values of particle filter algorithm are close to the true values and the error is comparatively small. Therefore, the particle filter algorithm has high accuracy in the SOC estimation for EV batteries.

Keywords


Electric vehicles, Battery data, State of Charge estimation, Particle filter algorithm


DOI
10.12783/dtcse/smce2017/12457

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