Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Wen-Jing Shen and Han-Xiong Li
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Wen-Jing Shen: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong, China
Han-Xiong Li: Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong, China

Energies, 2017, vol. 10, issue 4, 1-18

Abstract: 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: multi-scale parameter identification; lithium-ion battery (LIB); particle swarm optimization (PSO); Levenberg-Marquardt (LM) algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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