Multi-objective control and energy management strategy based on deep Q-network for parallel hybrid electric vehicles

To promote the energy management strategy of hybrid electric vehicles towards the direction of intelligence, this paper proposes a control model to design the learning-based energy management strategy for a parallel hybrid electric vehicle, which uses the deep Q-network (DQN) algorithm of deep reinforcement learning to control the engine and continuously variable transmission (CVT) synchronously. After completing the offline training of the control model, the near-optimal control strategy fitted by the neural network parameters is saved, and it is loaded and tested directly during the online test, which can reflect if the neural network has learned the mapping relationship between a random state and the optimal action. The simulation result of the online test shows that the DQN-based EMS can achieve a fuel economy of 5.31L/100km, and the consuming time is 1.67s when running the testing cycle of 1686s, which can ensure the real-time application potential, adaptability, and robustness.