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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
Ruixin Yang, Rui Xiong, Weixiang Shen, Xinfan Lin
工程（英文） ›› 2021, Vol. 7 ›› Issue (3) : 395-405.
PDF(3980 KB)
PDF(3980 KB)
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
Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit
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 ℃。
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.
电动汽车 / 电池安全 / 外部短路 / 温升预测 / 极限学习机
Electric vehicles / Battery safety / External short circuit / Temperature prediction / Extreme learning machine
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