On the applicability of advanced model-based strategies to control of electrified vehicle thermal systems

Electrified Vehicles (xEVs) often rely on complex thermal systems to meet energy efficiency and performance targets. These systems are typically made up of multiple interacting loops, such as coolant loop, oil loop, and refrigerant loop which can be highly nonlinear, strongly coupled, and teemed with multiple sources of uncertainties and disturbances. Designing effective control strategies for such complex thermal systems is a difficult and crucial task. This paper proposes three advanced Model-Based Control (MBC) strategies for the cabin active heating thermal system of an EV, namely, a Nonlinear Model Predictive Control (NMPC), a Model Predictive Control via Value Function Approximation (MPC-VFA), and a Linear-Quadratic Regulator (LQR). For this purpose, a physics-based model of the aforementioned thermal system is derived and validated using data generated from a high-fidelity thermal plant model created in GT-suite software. The parameters of the physics-based model are identified using the particle swarm optimization algorithm and it is shown that the nonlinear dynamics of the thermal system has been accurately captured. A comprehensive test study is performed and the performance of the proposed MBC approaches is evaluated using various indicators, such as reference tracking, energy consumption, robustness, computation time, and implementation complexity. Experimental data has been also utilized to validate the high-fidelity thermal plant model. The results of the test study demonstrate a high performance and efficiency of the proposed control strategies, offering significant advancements in thermal system control of Electrified Vehicles.