The safety of battery is a key factor restricting the development of electric vehicles. In recent years, accidents caused by power batteries have attracted a high degree of public attention. And external short circuit (ESC) of power battery is one of the reasons for the safety accident of electric vehicles. The paper proposes a method for the temperature of lithium-ion batteries of electric vehicles under external short circuit that combines the lumped parameter thermal model and the extreme learning machine. This method has the dual advantages of not requiring iterative parameter and model parameters with physical properties. Under the premise of greatly reducing the amount of calculation, the real-time and accurate prediction of the external short-circuit temperature rise of the power battery is realized, providing a foundation for the safety management of vehicle power batteries. Reference format: 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.

Ref：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.