Establishment of Reduced Order Models for Temperature Prediction in Electric Vehicle Charging Gun

Abstract With the rapid development of liquid cooled charging technology, thermal management of charging gun of electric vehicle has also become an urgent challenge to be solved. In this paper, an accurate simulation model is developed by correcting the thermal conductivity and heat generation of the model through the integration of experimental results. Subsequently, an experiment is designed with current, coolant inlet flow, coolant temperature, and ambient temperature as variables, from which a series of charging gun temperature simulation results are obtained. Utilizing these results as a training set, a reduced order model for predicting charging gun temperature is established. The influence of the multi-layer perceptron model, response surface model, and gaussian process model on prediction accuracy is then compared. The results indicate that compared to the other two models, the multi-layer perceptron model has a more significant advantage in fitting accuracy for nonlinear variables, with an average temperature prediction error of 1.61 °C. This study holds significant importance for the intelligent thermal management of charging devices.