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

Author

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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
    State Key Laboratory of High Performance Complex Manufacturing, School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China)

  • Han-Xiong Li

    (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, Central South University, Changsha 410083, China)

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.

Suggested Citation

  • Wen-Jing Shen & Han-Xiong Li, 2017. "Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm," Energies, MDPI, vol. 10(4), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:432-:d:94158
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    Citations

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    Cited by:

    1. En-Jui Liu & Yi-Hsuan Hung & Che-Wun Hong, 2021. "Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction," Energies, MDPI, vol. 14(3), pages 1-16, January.
    2. Korkmaz, Mehmet, 2024. "A novel approach for improving the performance of deep learning-based state of charge estimation of lithium-ion batteries: Choosy SoC Estimator (ChoSoCE)," Energy, Elsevier, vol. 294(C).

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