Energy optimization of electric vehicle's acceleration process based on reinforcement learning

Under the situation of unmanned driving, the energy consumption in an electric vehicle's acceleration process can be reduced by controlling the driving behavior. So in this paper, a pedal control strategy which could optimize the energy consumption of electric vehicle's acceleration process is proposed. The strategy is generated by the training results of reinforcement learning framework and the specific method of building such framework is discussed in details. Based on the training results of Q-learning-based algorithm, the relationship between the proportion of energy consumption reduction and vehicle's acceleration time is analyzed, which illustrates the energy-saving potential of the algorithm. In order to improve the control effect of the strategy, an updated algorithm framework based on Deep Q-learning (DQN) is proposed and an improved pedal's control strategy is obtained. Compared with the strategy obtained by Q-learning-based algorithm, the improved strategy not only achieves the same energy-saving effect, but also guarantees the stability of control effect, which is more suitable for actual use.