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.