Multi-objective optimization of hybrid electric vehicles energy management using multi-agent deep reinforcement learning framework

• A MADRL algorithm is used to realize the optimization of multi-objective energy management in hybrid electric vehicles. • Local information can be utilized by each agent within the energy management system to achieve optimized control. • Different strategies and driving cycles are employed to assess the optimization objectives of the energy management system. • Comparison between simulation and experiment is adopted to verify the real-time performance of the energy management strategy. Hybrid electric vehicles (HEVs) have the advantages of lower emissions and less noise pollution than traditional fuel vehicles. Developing reasonable energy management strategies (EMSs) can effectively reduce fuel consumption and improve the fuel economy of HEVs. However, current EMSs still have problems, such as complex multi-objective optimization and poor algorithm robustness. Herein, a multi-agent reinforcement learning (MADRL) framework is proposed based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve such problems. Specifically, a vehicle model and dynamics model are established, and based on this, a multi-objective EMS is developed by considering fuel economy, maintaining the battery State of Charge (SOC), and reducing battery degradation. Secondly, the proposed strategy regards the engine and battery as two agents, and the agents cooperate with each other to realize optimal power distribution and achieve the optimal control strategy. Finally, the WLTC and HWFET driving cycles are employed to verify the performances of the proposed method, the fuel consumption decreases by 26.91 % and 8.41 % on average compared to the other strategies. The simulation results demonstrate that the proposed strategy has remarkable superiority in multi-objective optimization.