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Engineering

2021, Volume 7,  Issue 3, Pages 395-405
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    Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

    a National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    b Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
    c Department of Mechanical and Aerospace Engineering, University of California, Davis, CA 95616, USA

    Received：2020-03-12 Revised：2020-06-02 Accepted： 2020-08-11 Available online：2020-11-14
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    10.1016/j.eng.2020.08.015
    Cite this article
    Ruixin Yang, Rui Xiong, Weixiang Shen, Xinfan Lin.Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit[J].Engineering,2021,7(3):395-405.

    Abstract

    External short circuit (ESC) of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles. In this study, a novel thermal model is developed to capture the temperature behavior of batteries under ESC conditions. Experiments were systematically performed under different battery initial state of charge and ambient temperatures. Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT) model to depict battery temperature behavior under ESC, where a lumped-state thermal model was used to replace the activation function of conventional ELMs. To demonstrate the effectiveness of the proposed model, we compared the ELMT model with a multi-lumped-state thermal (MLT) model parameterized by the genetic algorithm using the experimental data from various sets of battery cells. It is shown that the ELMT model can achieve higher computational efficiency than the MLT model and better fitting and prediction accuracy, where the average root mean squared error (RMSE) of the fitting is 0.65 °C for the ELMT model and 3.95 °C for the MLT model, and the RMES of the prediction under new data set is 3.97 °C for the ELMT model and 6.11 °C for the MLT model.

    Keywords

    Electric vehicles ; Battery safety ; External short circuit ; Temperature prediction ; Extreme learning machine
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