Reinforcement Learning-Based Energy Optimization Control for Hybrid Vehicles with P2 Structure
A reinforcement learning-based adaptive equivalent fuel consumption minimization strategy(RL-ECMS)is proposed for P2-structured hybrid vehicles,which realizes adaptive updating of the equivalence factor and dynamic allocation of vehicle torque through two intelligences to adapt to the changing driving demands.The RL-ECMS is compared with the conventional ECMS and rule-based control strategies through the MATLAB/Simulink simulation platform.The results show that the RL-ECMS can achieve lower fuel consumption under both FTP75 and FTP75-Highway typical driving conditions without affecting vehicle performance.The untrained ECE typical conditions are also tested,and the result shows that the algorithm proposed in this paper also has good generalization and robustness.
hybrid vehiclesenergy optimizationreinforcement learningaadaptive control
万方数据
维普
