Multi-objective energy management for off-road hybrid electric vehicles via nash DQN

Energy management strategies (EMSs) play a critical role in determining the performance of hybrid electric vehicles (HEVs). In the context of an off-road HEV equipped with two power sources—an engine-generator set and a hybrid energy storage system—one of the key challenges is optimizing energy distribution while balancing multiple objectives. This paper addresses this challenge by developing a multi-objective strategy based on a novel multi-agent reinforcement learning algorithm, specifically the Nash deep Q-network (Nash DQN). The proposed EMS seeks to improve fuel efficiency, maintain the battery state of charge, and extend battery lifespan while meeting the ultracapacitor SOC constraint. The Nash DQN algorithm, which integrates concepts from game theory and deep reinforcement learning, is employed to solve the multi-objective optimization problem. In this framework, the EGS and HESS are treated as individual agents, and their interactions are modeled as general-sum stochastic games. Through the application of Nash DQN’s theory, the two agents generate optimal actions that ensure Nash equilibrium, thus achieving a balanced trade-off between the various objectives. Finally, the performance of Nash DQN is evaluated through simulations, where it is compared to conventional DQN and dynamic programming approaches. The results demonstrate the superiority of Nash DQN in addressing the multi-objective optimization for HEV energy management.