A Surrogate-Assisted Teaching-Learning-Based Optimization for Parameter Identification of The Battery Model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
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Original languageEnglish
Pages (from-to)5909-5918
Journal / PublicationIEEE Transactions on Industrial Informatics
Issue number9
Online published18 Nov 2020
Publication statusPublished - Sep 2021


Lithium-ion batteries are widely used as power sources in industrial applications. Electrochemical models and simulations are crucial to disclose many details that cannot be directly measured through experiments. Parameter identification of an accurate electrochemical model is much more cost-effective than direct and destructive measurement methods. However, the complex structure and strong nonlinearity of electrochemical models will make the parameter identification very difficult. Additionally, time-consuming electrochemical simulations can significantly limit the identification efficiency. This paper proposes a surrogate-model-based scheme to achieve high-efficiency parameter identification of an electrochemical battery model. To be specific, the proposed method is implemented by the close integration of an evolutionary algorithm and a surrogate model. A sensitivity-based identification strategy is first designed to alleviate the difficulty of optimization. Then, a surrogate model is developed from historical data to gradually approach the objective function used for parameter evaluations. Finally, an evolutionary algorithm is employed to find promising solutions by minimizing the output of the surrogate model. Simulations and experimental studies demonstrate the effectiveness and high efficiency of the proposed method.

Research Area(s)

  • Batteries, Computational modeling, Data models, Electrodes, evolutionary algorithm, Integrated circuit modeling, Lithium-ion battery, Mathematical model, Optimization, parameter identification, surrogate model

Citation Format(s)