Electric vehicle predictive thermal comfort management with solar load estimation

Electric vehicles (EV) present distinctive challenges compared to ICE (Internal Combustion Engine) powered counterparts. Cabin heating and air-conditioning stand out among them, especially cabin heating in cold weather, owing to its outsized effect on drivable range of the vehicle. Efficient management of the cabin thermal system has the potential to improve vehicle range without compromising passenger comfort. A method to improve cabin thermal system regulation by effectively leveraging the solar load on the vehicle is proposed in this work. The methodology utilizes connectivity and mapping data to predict the solar load over a future time horizon. Typically, the solar load is treated as an unmeasured external disturbance which is compensated with control. It can however be treated as an estimated quantity with potential to enable predictive control. The solar load prediction, coupled with a passenger thermal comfort model, enables preemptive thermal system control over a route. A predictive control architecture is used to generate the HVAC control inputs for actuators of the cabin thermal system based on solar load preview information. This framework effectively leverages connectivity-enabled prediction and establishes novel means to include solar-load in the cabin thermal control. A simulation study is presented to demonstrate the effectiveness of the proposed method in improving temperature regulation performance.