Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle

A reinforcement learning-based adaptive energy management (RLAEM) is proposed for a hybrid electric tracked vehicle (HETV) in this paper. A control oriented model of the HETV is first established, in which the state-of-charge (SOC) of battery and the speed of generator are the state variables, and the engine´s torque is the control variable. Subsequently, a transition probability matrix is learned from a specific driving schedule of the HETV. The proposed RLAEM decides appropriate power split between the battery and engine-generator set (EGS) to minimize the fuel consumption over different driving schedules. With the RLAEM, not only is driver´s power requirement guaranteed, but also the fuel economy is improved as well. Finally, the RLAEM is compared with the stochastic dynamic programming (SDP)-based energy management for different driving schedules. The simulation results demonstrate the adaptability, optimality, and learning ability of the RLAEM and its capacity of reducing the computation time.