A temperature prediction method for electric vehicle charging connectors using a CFD-based deep learning model

2025-07-07

PRE

Yinghuai Liang

Panlong Liu

Shuhong Li *

李延军 (Yanjun Li)

Jiandong Wang

Yuefeng Pang

DOI：10.1016/j.ijthermalsci.2025.110139

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Abstract

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• A data-driven temperature prediction approach for electric vehicle connectors is proposed. • The effects of different resistance, ambient temperature, and current are discussed. • Temperature prediction using LSTM optimized by different population algorithms is analyzed. • The prediction performance of models based on the proposed algorithm is strengthened.

Keywords:

temperature prediction

electric vehicle connectors

LSTM

population algorithms

data-driven approach

A temperature prediction method for electric vehicle charging connectors using a CFD-based deep learning model

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