A Novel Energy Management Strategy Integrating Deep Reinforcement Learning and Rule Based on Condition Identification

Abstract

In order to reduce the driving cost of plug-in hybrid electric vehicles, a novel energy management strategy integrating deep reinforcement learning and rule based on condition identification is proposed. Firstly, principal component analysis is used to reduce the dimension of kinematic sequences features, and fuzzy clustering is used to classify the kinematics sequences, and then the working conditions are reorganized according to the classification results to obtain the three kinds of training conditions, and learning vector quantization neural network is used to train and identify the type of working conditions. Then, the rule taking engine torque, state of charge, and motor torque as control variables and driving mode as output is designed, which is integrated into the agent of deep reinforcement learning. Combined with the designed energy management strategy trained under three working conditions, the numerical simulation is carried out under the verification working condition based on condition identification, the simulation results are systematically analyzed to show the effectiveness of the designed energy management strategy.