Real-Time Thermal Comfort-Oriented NMPC for Electric Vehicle Heat Pump Systems Using a Control-Oriented PMV Model

Abstract

Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for passenger thermal comfort. This study proposes a real-time thermal comfort-oriented nonlinear model predictive control (NMPC) framework for EV heat pump systems. To enable real-time implementation, a control-oriented model is developed by combining a data-driven model for heat pump performance with a linearized Predicted Mean Vote (PMV) model. The proposed NMPC optimizes the trade-off between passenger thermal comfort and compressor energy consumption while satisfying actuator constraints. A high-fidelity physics-based virtual plant is employed to evaluate the proposed strategy. Simulation results show that the proposed NMPC maintains thermal comfort within the recommended PMV range while reducing total energy consumption by 8.3% compared with a conventional rule-based controller. Furthermore, a parametric study involving 1210 gain-tuning cases of a rule-based controller and 100 NMPC weighting scenarios demonstrates a consistently superior comfort–energy trade-off. The average computation time remains below 0.15 s on the simulation platform, indicating that the proposed NMPC formulation can be solved within the selected sampling interval. These results highlight the potential of thermal comfort-oriented NMPC for improving both passenger comfort and energy efficiency in EV heat pump systems.