Journal of System Simulation
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.
Recommended Citation
Fei, Xia; Wang, Zhicheng; Hao, Shuotao; Peng, Daogang; Yu, Beili; and Huang, Yimin
(2020)
"State of Charge Estimation of the Lithium-Ion Battery Based on Improved Extended Kalman Particle Filter Algorithm,"
Journal of System Simulation: Vol. 32:
Iss.
1, Article 6.
DOI: 10.16182/j.issn1004731x.joss.17-0448
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol32/iss1/6
First Page
44
Revised Date
2018-05-30
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.17-0448
Last Page
53
CLC
TM912
Recommended Citation
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.
DOI
10.16182/j.issn1004731x.joss.17-0448
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