Reinforcement Learning Based on Equivalent Consumption Minimization Strategy for Optimal Control of Hybrid Electric Vehicles
2020 · IEEE Access
저자
Heeyun Lee, 차석원 (Suk Won)
연구주제
Electric and Hybrid Vehicle Technologies · Electric Vehicles and Infrastructure · Vehicle Dynamics and Control Systems
NTIS 과학기술표준분류
에너지/자원만에너지·환경·원자력 확장
가장 가까운 분류(기계 > 자동차/철도차량)가 에너지·환경·원자력 확장 범위 밖이다.
AI 연구 키워드·방법
equivalent consumption minimization strategy 강화학습
키워드 (OpenAlex)
hybrid electric vehicles energy management strategy equivalent consumption minimization strategy reinforcement learning model-based reinforcement learning parallel hybrid electric vehicle fuel economy adaptive energy management
초록
Hybrid electric vehicles, operated by engines and motors, require an energy management strategy to achieve competitive fuel economy performance. The equivalent consumption minimization strategy is a well-known algorithm that can be employed for the energy management of hybrid electric vehicles, based on the concept of the equivalent cost of fossil fuels and electric battery energy. However, in the equivalent consumption minimization strategy approach, a parameter called the equivalent factor should be determined to obtain the optimal control policy. In this study, reinforcement learning based approaches are proposed to determine the equivalent factor. First, we show that the equivalent factor can be indirectly extracted from the reinforcement learning results, using the control action from reinforcement learning for the specific driving cycle. In addition, a novel approach that combines …
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
| 연도 | 과제 | 부처 | 사업 | 수행기관 | NTIS 분류(1순위) |
|---|---|---|---|---|---|
| 2021 | 창의융합형 세계선도 기계공학 미래인재 교육연구단 | 교육부 | 4단계두뇌한국21사업(R&D) | 서울대학 | – |
인접 그래프
노드 hover = 이웃 강조 · 1회 클릭 = 고정 · 고정된 노드 다시 클릭 = 페이지 이동
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교수 1
연구주제 1
Electric and Hybrid Vehicle Technologies
키워드 2
에너지 관리 전략(EMS) · 하이브리드 전기차(HEV)