Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle

The configuration and development of energy management strategies (EMSs) are generating considerable in-terest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV.

Automated summary

This study investigates a novel electric-hydraulic hybrid electric vehicle (EHHEV) and establishes a rule-based energy management strategy based on the hybrid system's energy flow. It combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided energy management system, achieving optimal switching among working modes and significantly improving vehicle economy.