Data-driven reinforcement learning?based real-time energy management system for plug-in hybrid electric vehicles [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 629.892 Robots
• Thông tin xuất bản: Golden, Colo. : Oak Ridge, Tenn. : National Renewable Energy Laboratory (U.S.) ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2016
• Mô tả vật lý: Size: 23 p. : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 265946
• Chủ đề: 1. 33 advanced propulsion systems; 2. Approximate dynamic programming; 3. Energy management system (ems); 4. Plug-in hybrid electric vehicles (phev); 5. Reinforcement learning (rl)

Tóm tắt

 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.