With the rapid development of new energy vehicles, high-power charging technology
has become an effective way to meet the fast-charging needs of electric
vehicles. Temperature control of charging cables is crucial for the safety and
efficiency of charging. This article aims to develop finite element method
(FEM)-ML to predict the temperature field of the charging cable. First, the
initial ambient temperature and maximum current were set as the main influencing
factors, and a dataset including various charging parameters and cable
temperature fields was built by FEM based on a two-factor, four-level orthogonal
design. Then, surrogate models based on the Bayesian optimization (BO)
algorithm, multilayer perceptron (MLP) model, and extreme gradient boosting
(XGB) model were established to predict the temperature field distribution of
high-power charging cables. The results indicated that the XGB model had better
prediction performance than the MLP model, with average values of MSE, RMSE,
MAE, and R
2 being 0.126, 0.351, 0.176, and 0.998, respectively. The XGB model
maintained prediction errors within 15% in both extrapolation and interpolation
schemes, demonstrating good generalization performance. Moreover, the XGB model
achieved a prediction speed more than 96% faster than that of FEM predictions,
significantly enhancing the prediction efficiency. The feature importance
analysis revealed that the maximum current has a more significant impact on the
temperature distribution of the cable than the initial temperature. This study
provides an efficient and accurate solution for predicting the temperature field
of high-power liquid-cooled charging cables.
