Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

• 作者: Ruixin Yang Rui Xiong Weixiang Shen Xinfan Lin
• 来源期刊: 工程（英文）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Energy Batteries—Article
• 页码范围: 395-405
• 页数: 11页
• 语种: 英文

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 ini-tial 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 conven-tional 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 experimen-tal data from various sets of battery cells.It is shown that the ELMT model can achieve higher computa-tional 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 ℃ for the ELMT model and 3.95 ℃ for the MLT model,and the RMES of the prediction under new data set is 3.97 ℃ for the ELMT model and 6.11 ℃ for the MLT model.