Research on Engine Thermal Management System Modeling and the Control Strategies of an Extended-Range Hybrid Electric Vehicle

A mathematical model of the thermal management system (TMS) for an extended-range hybrid electric vehicle is developed. The variation in engine coolant temperature is examined under different water pump and fan control strategies, and its subsequent impact on engine TMS energy consumption is analyzed. Based on the simulation results of energy consumption under various control parameters, machine learning regression models are constructed, and four different regression algorithms are applied. By incorporating temperature-based optimization into the water pump and fan control strategy, system energy consumption can be effectively reduced. The machine learning regression results indicate that the mathematical model of TMS cannot be simply regarded as a linear model. ANN and SVM regression show high degree of agreement with the mathematical model. This study provides a theoretical foundation for the development of data-driven tool for optimizing real-time TMS control strategies.