Model Predictive Control in Hybrid Thermal Management Systems for Improving Performance and Efficiency in Battery Electric Vehicles

In the pursuit of higher energy efficiency and improved overall system performance, Model Predictive Control (MPC) presents a promising approach for managing the complex dynamics of hybrid thermal management systems. This presentation explores the application of MPC in such systems, focusing on how it can significantly reduce actuator energy consumption while maintaining or even enhancing thermal and electrical efficiency.

The discussion begins with the motivation behind choosing MPC over traditional control strategies. Hybrid thermal management systems typically involve multiple components, operating modes, and competing performance objectives, all under varying environmental and operational conditions. Conventional rule-based controllers, while simple and robust, often lack the adaptability and foresight required to optimize performance in these dynamic environments. MPC, in contrast, enables predictive, constraint-aware, and multi-variable optimization, making it particularly suitable for these challenges.

To demonstrate its advantages, a hybrid thermal system is developed, incorporating various cooling modes and key components that reflect real-world applications. A conventional control method is implemented as a baseline for comparison. The system is then modeled using a physics-based approach, ensuring that thermal and electrical dynamics are accurately captured. Particular attention is paid to the model validation process, as the predictive accuracy of MPC is highly dependent on the quality of the underlying model.

The controller is designed and implemented in a digital development environment that allows for simulation, testing, and tuning. Central to the MPC framework is the cost function, which is carefully constructed to balance objectives such as minimizing energy consumption, maintaining thermal constraints, and ensuring stable operation. The parametrization of the controller plays a crucial role in its success, as tuning directly impacts stability, prediction accuracy, real-time feasibility, and the degree to which operational goals are achieved.

After careful development and calibration, the MPC is benchmarked against the conventional rule-based controller across several performance indicators, including computational time, thermal control effectiveness, and overall electrical efficiency. The results clearly demonstrate the potential of MPC to deliver improved control quality and reduced energy consumption in hybrid thermal systems.

The presentation concludes with an evaluation of the controller’s real-world applicability and outlines future steps aimed at further enhancing robustness and integration into more complex thermal management architectures.