Study on Power Management Strategy of HEV using Dynamic Programming
2016 · World Electric Vehicle Journal
저자
Heeyun Lee, Changbeom Kang, Yeong-il Park, 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
artificial neural network 동적 계획법 stochastic dynamic programming
키워드 (OpenAlex)
hybrid electric vehicles energy management strategy dynamic programming fuel economy stochastic dynamic programming artificial neural networks rule-based control parallel hybrid electric vehicle
초록
For hybrid electric vehicle, it is necessary to control power distribution among multiple power sources to improve fuel economy performance of vehicle. In this paper, power management strategy of hybrid electric vehicle using Dynamic programming is studied. Deterministic dynamic programming could present outstanding fuel economy, while its application as real time control of vehicle is limited. Thus, different kinds of power management strategy using dynamic programming are studied. Stochastic dynamic programming, artificial neural networks and rule-based power management strategy using results from dynamic programming are studied. Simulations using parallel type hybrid electric vehicle model are conducted. Simulation results including fuel economy performance on diverse driving cycles are compared and analysed.
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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)