인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
초록·키워드
Technological developments enable low-carbon transitions to be accelerated by conceptualization systems and innovations for research and development to generate clean energy. Batteries are becoming one of the essential parts of the science of electrical power sources. Lithium-ion batteries are part of the change and development factors in technologies that significantly impact the portable devices sector and the development of electric vehicles. Designing the material structure and composition of battery manufacturing with the help of engineering system design will form a much more optimal battery. Machine learning algorithms can easily optimize the battery’s composition through battery experiment test data history to produce a more optimal battery configuration. This study is prepared to identify research gaps in topics related to machine learning for battery optimization. Related studies about machine learning for battery optimization are identified using bibliometric analysis and systematic literature review of the study search index through database Scopus-indexed publications. The results from this paper reveal energy management systems and strategies, hybrid vehicles, other optimization algorithms, battery electrodes, and the safety of batteries as the particular research gap according to machine learning for battery optimization. This paper expects research on battery optimization using machine learning methods will continue to be developed to maximize the potential of machine learning algorithms in helping the research process.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
