Powertrain Hybridization for Autonomous Vehicles: Fuel Efficiency Perspective in Mixed Autonomy Traffic

Today’s hybrid electric vehicles (HEVs) are designed with human drivers in mind; therefore, they may not be the optimal choice for future autonomous vehicles (AVs), which can precisely adjust their velocity profile to minimize energy loss. In this study, we analyze the required degree of hybridization for AVs versus human drivers from a fuel economy (FE) perspective by utilizing real-world urban driving profiles and creating equivalent AV drive cycles in a mixed autonomy car-following scenario. We solve the optimal energy management problem for HEVs with a range of motor sizes available on the automotive market, and demonstrate that while human drivers typically need a motor size of about 30 kW to maximize the benefits of hybridization, AVs can achieve comparable advantages with just a 12-kW motor. This increased efficiency from a smaller motor can be attributed to a more optimal torque request, enhancing gains from regenerative braking and improving engine efficiency. Furthermore, we explore the benefits of velocity smoothing for both traditional vehicles and HEVs, and examine various mechanisms that contribute to reducing fuel consumption (FC). Our findings indicate that velocity smoothing yields more significant benefits for HEVs with smaller motors compared to non-hybrid vehicles and HEVs with larger motors.