Improving Hybrid Vehicle Fuel Efficiency Using Inverse Reinforcement Learning

Deciding what mix of engine and battery power to use is critical hybrid vehicles' fuel efficiency. Current solutions consider several factors such as the charge how efficient operates at a given speed. Previous research has shown that by taking into account future requirements vehicle, more balance vs. can be attained. In this paper, we utilize probabilistic driving route prediction system, trained using Inverse Reinforcement Learning, optimize control policy. Our approach considers routes driver likely taking, computing an optimal power. This potential increase vehicle efficiency while not requiring any hardware modification or change in behavior. method outperforms standard policy, yielding average 1.22% savings.