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Deep Reinforcement Learning for Advanced Energy Management of Hybrid Electric Vehicles

Machine Learning seizes a substantial role in the development of future low-emission automobiles, as manufacturers are increasingly reaching limits with traditional engineering methods. Apart from autonomous driving, recent advances in reinforcement learning also offer great benefit for solving complex parameterization tasks. In this paper, deep reinforcement learning is used for the derivation of efficient operating strategies for hybrid electric vehicles. There, for achieving fuel efficient solutions, a wide range of potential driving and traffic scenarios have to be anticipated where intelligent and adaptive processes could bring significant improvements. The underlying research proves the ability of a reinforcement learning agent to learn nearlyoptimal operating strategies without any prior route-information and offers great potential for the inclusion of further variables into the optimization process.