State of Charge Estimation of the Lithium-Ion Battery Based on Improved Extended Kalman Particle Filter Algorithm

doi:10.16182/j.issn1004731x.joss.17-0448

Abstract

Abstract: The three order Thevenin model of 18650 Lithium-Ion battery is established based on the experimental data of UTS divided capacity tester. The extended kalman filtering (EKF) algorithm is adopted as the important density function of particle filter (PF) algorithm, and the extended Kalman particle filter (EKPF) algorithm is formed. The sample degradation and lack of diversity in the re-sampling stage of EKPF algorithm is optimized by an improved re-sampling algorithm which based on a weight sorting and survival of the fittest particles. The improved EKPF algorithm is applied to estimate the State of Charge (SOC) of the three order Thevenin model of batteries. The experimental results show that the SOC estimation accuracy of the improved EKPF algorithm is better than that of the EKF algorithm and the PF algorithm.

Key words: SOC(State of Charge) estimation, improved EKPF algorithm, re-sampling, weight sorting

CLC Number:

TM912

Xia Fei, Wang Zhicheng, Hao Shuotao, Peng Daogang, Yu Beili, Huang Yimin. State of Charge Estimation of the Lithium-Ion Battery Based on Improved Extended Kalman Particle Filter Algorithm[J]. Journal of System Simulation, 2020, 32(1): 44-53.

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