Deep reinforcement learning based energy management for a hybrid electric vehicle

This research proposes a reinforcement learning-based algorithm and a deep reinforcement learning-based algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding energy management formulation is established. Subsequently, a new variant of reinforcement learning (RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated by comparing with DP and conventional Dyna method. Facing the problem of the “curse of dimensionality” in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Q-learning (DQL) is designed for energy management control, which uses a new optimization method (AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning control system is trained and verified by the realistic driving condition with high-precision, and is compared with the benchmark method DP and the traditional DQL method. Results show that the proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption than traditional DQL policy does, and its fuel economy quite approximates to global optimum. Furthermore, the adaptability of the proposed method is confirmed in another driving schedule.