지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering Vol.41 No.5
- 발행연도
- 2024.5
- 수록면
- 355 - 364 (10page)
- DOI
- 10.7736/JKSPE.024.020
이용수
초록· 키워드
오류제보하기
Environmental issues have become a global concern recently. Countries worldwide are making efforts for carbon neutrality. In the automotive industry, focus has shifted from internal combustion engine vehicle to eco-friendly vehicles such as Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), and Fuel Cell Electric Vehicles (FCEVs). For driving strategy, research on vehicle driving method that can reduce vehicle energy consumption, called eco-driving, has been actively conducted recently. Conventional cruise mode driving control is not considered an optimal driving strategy for various driving environments. To maximize energy efficiency, this paper conducted research on eco-driving strategy for EVs-based on reinforcement learning. A longitudinal dynamics-based electric vehicle simulator was constructed using MATLAB Simulink with a road slope. Reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Deep QNetwork (DQN), were applied to minimize energy consumption of EVs with a road slope. The simulator was trained to maximize rewards and derive an optimal speed profile. In this study, we compared learning results of DDPG and DQN algorithms and confirmed tendencies by parameters in each algorithm. The simulation showed that energy efficiency of EVs was improved compared to that of cruise mode driving.
목차
1. 서론
2. 전기차 시뮬레이션 모델 개발
3. 강화학습 알고리즘
4. 시뮬레이션 결과
5. 결론
REFERENCES
참고문헌 (17)
참고문헌 신청
Dib, W., Chasse, A., Moulin, P., Sciarretta, A., Corde, G., (2014), Optimal energy management for an electric vehicle in eco-driving applications, Control Engineering Practice, 29, 299-307.
Lee, H. Y., Kim, N. W., Cha, S. W., (2020), Model-based reinforcement learning for eco-driving control of electric vehicles, Journal of the Institute of Electrical and Electronics Engineers Access, 8, 202886-202896.
Xu, N., Li, X., Liu, Q., Zhao, D., (2021), An overview of eco-driving theory, capability evaluation, and training applications, Sensors, 21(19), 6547.
Mensing, F., Bideaux, E., Trigui, R., Tattegrain, H., (2013), Trajectory optimization for eco-driving taking into account traffic constraints, Transportation Research Part D, 18(1), 55-61.
Mahler, G., Vahidi, A., (2014), An optimal velocity-planning scheme for vehicle energy efficiency through probabilistic prediction of traffic-signal timing, Journal of the Institute of Electrical and Electronics Engineers Transactions on Intelligent Transportation System, 15(6), 2516-2523.
Lee, H. Y., Kim, N. W., Cha, S. W., (2020), Model-based reinforcement learning for eco-driving control of electric vehicles, Journal of the Institute of Electrical and Electronics Engineers Access, 8, 202886-202896.
Xu, N., Li, X., Liu, Q., Zhao, D., (2021), An overview of eco-driving theory, capability evaluation, and training applications, Sensors, 21(19), 6547.
Mensing, F., Bideaux, E., Trigui, R., Tattegrain, H., (2013), Trajectory optimization for eco-driving taking into account traffic constraints, Transportation Research Part D, 18(1), 55-61.
Mahler, G., Vahidi, A., (2014), An optimal velocity-planning scheme for vehicle energy efficiency through probabilistic prediction of traffic-signal timing, Journal of the Institute of Electrical and Electronics Engineers Transactions on Intelligent Transportation System, 15(6), 2516-2523.
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