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《工程(英文)》 >> 2021年 第7卷 第3期 doi: 10.1016/j.eng.2020.08.015

基于极限学习机的电动汽车锂离子动力电池外部短路热模型研究

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

收稿日期: 2020-03-12 修回日期: 2020-06-02 录用日期: 2020-08-11 发布日期: 2020-11-14

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摘要

锂离子动力电池外部短路(ESC)是电动汽车常见且严重的电气故障之一。为此,本文提出了一种新型电池热模型以准确刻画动力电池外部短路发生后的温度行为。主要工作如下:在不同电池荷电状态和环境温度下,设计并系统地开展了动力电池外部短路实验;为保证模型参数的物理意义和模型的精准性,利用集总参数热模型替换经典极限学习机中的激活函数,构建了基于极限学习机的电池热(ELMT)模型,实现了模型无需迭代调节参数和模型参数就可以具备物理属性的双重优势,极大提高了模型计算效率与准确度;为了评估模型改进的必要性,比较了极限学习机热模型与遗传算法参数化的多集总参数热(MLT)模型。结果表明,ELMT模型相比MLT模型具有更优异的计算效率以及拟合、预测精度。其中,ELMT模型拟合和预测电池温度的均方根误差分别为0.65 ℃和3.95 ℃,MLT模型拟合和预测电池温度的均方根误差分别为3.97 ℃和6.11 ℃。

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