LAPSE:2019.1492
Published Article
LAPSE:2019.1492
Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm
Wen-Jing Shen, Han-Xiong Li
December 10, 2019
This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB) electric model by using a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD) of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.
Keywords
Levenberg-Marquardt (LM) algorithm, lithium-ion battery (LIB), multi-scale parameter identification, particle swarm optimization (PSO)
Suggested Citation
Shen WJ, Li HX. Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm. (2019). LAPSE:2019.1492
Author Affiliations
Shen WJ: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong, China; State Key Laboratory of High Performance Complex Manufacturing, School of Mechanical and Electrical Engineering, [ORCID]
Li HX: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong, China; State Key Laboratory of High Performance Complex Manufacturing, School of Mechanical and Electrical Engineering,
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Journal Name
Energies
Volume
10
Issue
4
Article Number
E432
Year
2017
Publication Date
2017-03-27
Published Version
ISSN
1996-1073
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Original Submission
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PII: en10040432, Publication Type: Journal Article
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LAPSE:2019.1492
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doi:10.3390/en10040432
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Dec 10, 2019
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Dec 10, 2019
 
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Calvin Tsay
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