  Learning-based intelligent energy management systems for plug-in hybrid
electric vehicles (PHEVs) are crucial for achieving efficient energy
utilization. However, their application faces system reliability challenges in
the real world, which prevents widespread acceptance by original equipment
manufacturers (OEMs). This paper begins by establishing a PHEV model based on
physical and data-driven models, focusing on the high-fidelity training
environment. It then proposes a real-vehicle application-oriented control
framework, combining horizon-extended reinforcement learning (RL)-based energy
management with the equivalent consumption minimization strategy (ECMS) to
enhance practical applicability, and improves the flawed method of equivalent
factor evaluation based on instantaneous driving cycle and powertrain states
found in existing research. Finally, comprehensive simulation and
hardware-in-the-loop validation are carried out which demonstrates the
advantages of the proposed control framework in fuel economy over adaptive-ECMS
and rule-based strategies. Compared to conventional RL architectures that
directly control powertrain components, the proposed control method not only
achieves similar optimality but also significantly enhances the disturbance
resistance of the energy management system, providing an effective control
framework for RL-based energy management strategies aimed at real-vehicle
applications by OEMs.
