메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
SeongWan Kim (Korea Institute of Maritime and Fisheries Technology) SeokCheon Kang (Korea Maritime & Ocean University) MinKi Son (Korea Maritime & Ocean University) HyeonMin Jeon (Korea Maritime & Ocean University)
저널정보
한국마린엔지니어링학회 Journal of Advanced Marine Engineering and Technology (JAMET) 한국마린엔지니어링학회지 제46권 제6호
발행연도
2022.12
수록면
387 - 393 (7page)
DOI
10.5916/jamet.2022.46.6.387

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
With the international community"s efforts to reduce carbon dioxide, the technology to improve the energy efficiency of ships has been significantly advanced owing to the development of propulsion and power systems. Among them, the power management system in the electric propulsion system, which is a eco-friendly technology, has been transformed to an energy management system(EMS) that integrates the battery management system or green technologies to improve system efficiency. The application of EMS can improve the efficiency of the power system by applying this technology not only to the electric propulsion system but also to the power generation system in merchant ships using the conventional direct mechanical propulsion system. Optimum control of the power generation source according to the rule-based strategy, including the load and state of charge of the battery, referring to the designer"s intentions, is presented in this study. The neural network controller logic is developed, and the stability of the control system is analyzed using Matlab/Simulink based on various environments. As a test result, according to the step-by-step changes in load and battery status, the designed learning value shows a stable generator output command value in all operation areas and a stable generator optimal control output according to various conditions.

목차

Abstract
1. Introduction
2. Power management system and energy management system for ship
3. Design of energy management system using neural network
4. Conclusion
References

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2023-559-000333184