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Title: Data-driven reinforcement learning–based real-time energy management system for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [2]
  1. Univ. of California, Riverside, CA (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
OSTI Identifier:
1239892
Report Number(s):
NREL/JA-5400-65413
Journal ID: ISSN 0361-1981
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
Transportation Research Record
Additional Journal Information:
Journal Volume: 2572; Journal ID: ISSN 0361-1981
Publisher:
National Academy of Sciences, Engineering and Medicine
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
U.S. Department of Transportation
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; plug-in hybrid electric vehicles (PHEV); energy management system (EMS); approximate dynamic programming; reinforcement learning (RL)