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
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.
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
|Journal Article Type||Article|
|Acceptance Date||Jul 17, 2020|
|Online Publication Date||Aug 31, 2020|
|Publication Date||Oct 31, 2020|
|Deposit Date||Oct 15, 2020|
|Publicly Available Date||Oct 15, 2020|
|Journal||International Journal of Electrochemical Science|
|Publisher||Electrochemical Science Group|
|Peer Reviewed||Peer Reviewed|
|Keywords||Lithium-ion battery; State of charge; Particle filter; Particle swarm optimization; Importance resampling|
WANG 2020 A novel gaussian
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