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A novel gaussian particle swarms optimized particle filter algorithm for the state of charge estimation of lithium-ion batteries.

Wang, Xinyang; Wang, Shunli; Huang, Junhan; Fernandez, Carlos; Zhou, Yicong; Chen, Lei

Authors

Xinyang Wang

Shunli Wang

Junhan Huang

Carlos Fernandez

Yicong Zhou

Lei Chen



Abstract

A gaussian particle swarm optimized particle filter estimation method, along with the second-order resistance-capacitance model, is proposed for the state of charge estimation of lithium-ion battery in electric vehicles. Based on the particle filter method, it exploits the strong optimality-seeking ability of the particle swarm algorithm, suppressing algorithm degradation and particle impoverishment by improving the importance distribution. This method also introduces normally distributed decay inertia weights to enhance the global search capability of the particle swarm optimization algorithm, which improves the convergence of this estimation method. As can be known from the experimental results that the proposed method has stronger robustness and higher filter efficiency with the estimation error steadily maintained within 0.89% in the constant current discharge experiment. This method is insensitive to the initial amount and distribution of particles, achieving adaptive and stable tracking in the state of charge for lithium-ion batteries.

Journal Article Type Article
Publication Date Oct 31, 2020
Journal International Journal of Electrochemical Science
Print ISSN 1452-3981
Publisher Electrochemical Science Group
Peer Reviewed Peer Reviewed
Volume 15
Issue 10
Pages 10632-10648
Institution Citation WANG, X., WANG, S., HUANG, J., FERNANDEZ, C., ZHOU, Y. and CHEN, L. 2020. A novel gaussian particle swarms optimized particle filter algorithm for the state of charge estimation of lithium-ion batteries. International journal of electrochemical science [online], 15(10), pages 10632-10648. Available from: https://doi.org/10.20964/2020.10.21
DOI https://doi.org/10.20964/2020.10.21
Keywords Lithium-ion battery; State of charge; Particle filter; Particle swarm optimization; Importance resampling

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