TY  - INPR
UR  - http://dx.doi.org/10.1016/j.energy.2024.133196
ID  - discovery10197467
N2  - 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.
AV  - restricted
SN  - 0360-5442
Y1  - 2024/09/20/
TI  - Modeling and control system optimization for electrified vehicles: A data-driven approach
JF  - Energy
PB  - Elsevier BV
A1  - Zhang, Hao
A1  - Lei, Nuo
A1  - Chen, Boli
A1  - Li, Bingbing
A1  - Li, Rulong
A1  - Wang, Zhi
KW  - Plug-in hybrid electric vehicles; Energy management strategy; High-fidelity training environment; Reinforcement learning; Reliable control framework
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -