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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.

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.

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies.