关键词: 热管理；双层 LSTM 模型；TCN 模型；神经网络；性能预测

Abstract: The operating characteristics of vehicle thermal management systems are jointly affected by driving conditions, temperature settings and occupant comfort. An accurate performance prediction is critical for system optimization and fault diagnosis. This paper employs dual-layer long short-term memory (LSTM) and temporal convolutional network (TCN) for temperature prediction in electric vehicle thermal management systems, analyzes the mechanism of the two algorithms,verifies key modules through ablation experiments, and discusses the influences of data length, time window and training strategies on prediction performance.Using root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), concordance correlation coefficient (CCC) and training time as evaluation metrics, the models are trained and validated based on real-vehicle coolingdown data. The predicted trends of facevent air temperature and breath temperature are compared with measured data. Results show that the dual-layer LSTM model achieves higher accuracy with a CCC value of 0.94 for facevent air temperature. The TCN model exhibits outstanding advantages in training and inference efficiency: its training time is only 2.2% of the LSTM model, and the prediction speed is nearly 15 times higher, while the accuracy still meets engineering requirements. Ablation experiments demonstrate that the dual-layer structure and Dropout enhance the performance of LSTM, and dilated convolution combined with residual connections constitutes the core of efficient TCN modeling. The 30 s time window, 1 650 s data length and optimized training strategies achieve the best model performance. This study provides valuable references for the application of dual-layer LSTM and TCN in vehicle thermal management systems.