| 摘要: 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. |
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| | 图1 Battery ESC test bench. | 图2 Diagram of ELM based thermal model. | 引文信息: Yang R, Xiong R, Shen W, et al. Extreme Learning Machine Based Thermal Model for Lithium-ion Batteries of Electric Vehicles under External Short Circuit[J]. Engineering, 2020. (下载链接) | 其他相关论文: 1. 熊瑞,李幸港.基于双卡尔曼滤波算法的动力电池内部温度估计[J].机械工程学报,2020,56(14):146-151. (下载链接)
2. 熊瑞,马骕骁,杨瑞鑫,陈泽宇.动力电池外部短路故障热-力影响与分析[J].机械工程学报,2019,55(02):115-125. (下载链接)
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