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Energy management in electric vehicles plays a significant role in both reducing energy consumption and limiting the rate of battery capacity degradation. It is especially important for systems with multiple energy storage units where optimally arbitrating power demand among the energy storage units is challenging. While many optimal control methods exist for designing a good energy management system, in this work a Reinforcement-Learning (RL) methodology is explored to design an energy management system for an electric vehicle with a Hybrid Energy Storage System (HESS) that included a battery and a supercapacitor. The energy management system is designed to optimally divide the traction power request among a battery and a super-capacitor in real-time; while trying to minimize the overall energy consumption and battery degradation.

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.

DC fast charging (DCFC) also referred to as L3 charging, is the fastest charging technology to replenish the drivable range of an electric vehicle. DCFC provides the convenience of faster charging time compared to L1 and L2 at the expense of potentially increased battery health degradation. It is known to accelerate battery capacity fade leading to reduced range and lifetime of the EV battery. While there are active efforts and several means to reduce the downsides of DCFC at cell chemistry level, this trade-off is still an important consideration for most battery cells in automotive propulsion applications. Since DCFC is a customer driven technology, informing drivers of the trade-off of each DCFC event can potentially result in better outcomes for the EV battery life. Traditionally, the driver is advised to limit DCFC events without providing quantifiable metrics to inform their decisions during EV charging.