Reinforcement learning based power management for hybrid electric vehicles

Compared to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollution emissions. The HEV consists of a hybrid propulsion system containing one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and therefore requires advanced power management policies to achieve higher performance and lower fuel consumption. Towards this end, our work aims at minimizing the HEV fuel consumption over any driving cycle (without prior knowledge of the cycle) by using a reinforcement learning technique. This is in clear contrast to prior work, which requires deterministic or stochastic knowledge of the driving cycles. In addition, the proposed reinforcement learning technique enables us to (partially) avoid reliance on complex HEV modeling while coping with driver specific behaviors. To our knowledge, this is the first work that applies the reinforcement learning technique to the HEV power management problem. Simulation results over real-world and testing driving cycles demonstrate the proposed HEV power management policy can improve fuel economy by 42%.