Data-Driven Koopman Model of an Integrated HVAC and Battery Cooling System in Electric Vehicles

With the rapid advancements in connected and automated vehicles (CAV) technologies and vehicle onboard computational units, various model predictive control-based algorithms have emerged for electric vehicle (EV) thermal management systems. However, the nonlinear dynamics of refrigerant circuits, coupled with battery cooling systems, often require assumptions and simplifications when developing computationally inexpensive physics-based control-oriented models, and these approximations may lead to non-negligible prediction errors. To address this challenge, this paper proposes a data-driven Koopman model to capture the behavior of integrated HVAC and battery cooling systems in an EV. The proposed model is developed using the Extended Dynamic Mode Decomposition (EDMD) structure, leveraging the data acquired from a high-fidelity EV thermal management system (TMS) model. The dimension of the lifted space is investigated, considering both a corrected Akaike Information Criterion AIC c ) and open-loop prediction performance. The validation of the proposed model against the high-fidelity model shows its superiority over a physics-based model: the root mean square errors (RMSEs) for the refrigerant saturated temperature at the outdoor condenser and the evaporator are 1.51 °C and 5.13 °C, respectively; the RMSEs for mass-averaged battery temperature and battery coolant temperature are 0.10 °C and 0.67 °C, respectively.