eprintid: 10197467 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/19/74/67 datestamp: 2024-09-25 09:59:09 lastmod: 2024-09-25 09:59:09 status_changed: 2024-09-25 09:59:09 type: article metadata_visibility: show sword_depositor: 699 creators_name: Zhang, Hao creators_name: Lei, Nuo creators_name: Chen, Boli creators_name: Li, Bingbing creators_name: Li, Rulong creators_name: Wang, Zhi title: Modeling and control system optimization for electrified vehicles: A data-driven approach ispublished: inpress divisions: UCL divisions: B04 divisions: F46 keywords: Plug-in hybrid electric vehicles; Energy management strategy; High-fidelity training environment; Reinforcement learning; Reliable control framework note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: 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. date: 2024-09-20 date_type: published publisher: Elsevier BV official_url: http://dx.doi.org/10.1016/j.energy.2024.133196 full_text_type: other language: eng verified: verified_manual elements_id: 2320034 doi: 10.1016/j.energy.2024.133196 lyricists_name: Chen, Boli lyricists_id: BCHEB76 actors_name: Chen, Boli actors_id: BCHEB76 actors_role: owner full_text_status: restricted publication: Energy article_number: 133196 issn: 0360-5442 citation: Zhang, Hao; Lei, Nuo; Chen, Boli; Li, Bingbing; Li, Rulong; Wang, Zhi; (2024) Modeling and control system optimization for electrified vehicles: A data-driven approach. Energy , Article 133196. 10.1016/j.energy.2024.133196 <https://doi.org/10.1016/j.energy.2024.133196>. (In press). document_url: https://discovery.ucl.ac.uk/id/eprint/10197467/1/Manuscript_RL_ECMS.pdf