Temperature Prediction of Wet Clutch Friction Pair Based on Optuna-LSTM Neural Network

As critical actuating components in vehicular transmission systems, wet clutches exhibit strongly nonlinear thermal responses in their friction pairs during engagement operations. Although existing temperature prediction models achieve high-accuracy prediction performance, their practical application remains constrained by significant limitations such as high computational costs and time consumption. This study proposes an Optuna-LSTM temperature prediction model for wet clutch friction pairs, developed through the integration of long short-term memory (LSTM) deep learning theory with finite element method generated training datasets under diverse operating conditions. By synergistically combining the automated hyperparameter optimization library (Optuna) framework and early stopping mechanisms, the model enables dynamic temperature prediction of friction pairs. Experimental results indicate that the proposed model achieves prediction metrics of Root Mean Squared Error (RMSE) of 1.42 °C, Mean Absolute Error (MAE) of 1.09 °C, Coefficient of Determination (R2) of 0.9930, and Mean Absolute Percentage Error (MAPE) of 0.72% with a prediction duration of 60 ms. These findings confirm that the Optuna-LSTM model enables both accurate and rapid temperature prediction for friction pairs, providing an efficient solution for thermal management in wet clutch systems.