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Surrogate Model-Assisted Evolutionary Algorithms for Parameter Identification of Electrochemical Model of Lithium-Ion Battery: A Comparison Study

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1801))

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Abstract

Lithium-ion batteries are widely used in various fields due to their high energy density and long cycling life. However, over-charge, over-discharge, and over-heating will cause the battery’s performance to drop rapidly and even cause safety crises. The parameters of an electrochemical model are critical to making lithium-ion batteries operate safely because they can help indicate the internal states of the battery. Thus, it is significant to identify the parameters of the electrochemical model of lithium-ion batteries. The parameter identification of the electrochemical model of lithium-ion batteries is a complex expensive optimization problem intrinsically. Surrogate model-assisted evolutionary algorithms have been designed to solve this problem, but the choice of surrogate models is still an open question. A suitable surrogate model can reduce the number of time-consuming simulations of the electrochemical model; thus, it can improve the identification accuracy significantly. However, how to select a proper surrogate model has not been studied adequately. In view of this, this paper compares seven different surrogate models’ performance for parameter identification and aims to provide some insights for future researchers when choosing surrogate models. Extensive simulations show that the support vector regression (SVR) model would be a good choice to aid the parameter identification.

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Yan-Bo He & Bing-Chuan Wang

  2. College of Information Science and Electronic Engineering, Hunan University, Changsha, 410083, China

    Zhi-Zhong Liu

Authors
  1. Yan-Bo He
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  2. Bing-Chuan Wang
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Correspondence to Bing-Chuan Wang .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Wuhan University of Technology, Wuhan, China

    Dongming Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Lianghao Li

  4. Huazhong University of Science and Technology, Wuhan, China

    Jianqing Lin

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He, YB., Wang, BC., Liu, ZZ. (2023). Surrogate Model-Assisted Evolutionary Algorithms for Parameter Identification of Electrochemical Model of Lithium-Ion Battery: A Comparison Study. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_1

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  • DOI: https://doi.org/10.1007/978-981-99-1549-1_1

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  • Print ISBN: 978-981-99-1548-4

  • Online ISBN: 978-981-99-1549-1

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