Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method

Abstract

Energy management is a fundamental task of a hybrid electric vehicle. However, dealing with multiple hybrid electric vehicles would be very time consuming, and developing a separate management strategy for each model is a huge workload to. Based on the above problems, this paper investigates the generalization capability of energy management strategies for hybrid electric vehicles. To improve the generalization of energy management strategies, a multi-agent reinforcement learning algorithm is proposed. To achieve this goal, the first analysis from the state values of reinforcement learning in the state selection, if all the typical features of the vehicle operation are added to the reinforcement learning algorithm, then it will make the model have a certain generalization ability. Then, with the help of the auxiliary agent, the reward value of reinforcement learning can be improved by using KL-divergence. The training and validation results show that the strategy can also achieve the training effect when tested on new models. In addition, a new driving cycle is selected for environmental testing, and the results show that the method also has strong generalization ability.