Abstract: The energy management strategy based on reinforcement learning encounters the problem of “dimension disaster”when dealing with high-dimensional problems because of the discretization of state and control variables. For this problem, a new energy management algorithm based on deep reinforcement learning with normalized advantage function is proposed, where two deep neural networks with normalized advantage function are used to realize the continuous control of energy and eliminate the discretization of state and control variables. Based on the modeling of powertrain of a series hybrid tracked vehicle, the framework of the proposed deep reinforcement learning algorithm was built and the parameter update process was completed for the series hybrid tracked vehicle. The simulated results show that the proposed algorithm can output more refined control quantity and less output fluctuation. Compared with the deep Q-learning algorithm, the proposed algorithm improves the fuel economy of series hybrid tracked vehicle by 3.96%. In addition, the adaptability of the proposed algorithm and the optimized effect in real-time control environment are verified by the hardware-in-the-loop simulation.

Key words: serieshybridtrackedvehicle, energymanagementstrategy, normalizedadvantagefunction, continuouscontrol, hardware-in-the-loopsimulation