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
Recently, the popularity of li-ion batteries has attracted many researchers to carry out the battery’s maximum potential. Predicting batteries condition and behavior is part of the process that is considered challenging. ML algorithm is widely applied to overcome this challenge as it demonstrates a successful outcome in optimizing the complexity, accuracy, reliability, and efficiency of battery prediction. Yet, we believe there is a particular research area of battery prediction that can further be explored and enhanced with machine learning capability. Therefore, we perform a systematic literature review and bibliometric study to uncover the gap in the machine learning application in the battery prediction field. This study is divided into four stages: (1) literature search from the Scopus Database, (2) filtering the results based on keywords and prepared criteria using PRISMA method, (3) systematic review from filtered papers to provide further understanding, and (4) bibliometric analysis from visualization created in VOSViewer software . The analysis findings determine battery safety and performance prediction as a potential gap in the scope of machine learning for battery prediction research and provide some insightful information to assist future researchers. We envision this study to encourage further battery research, which will assist in the creation of better, cleaner, safer, and long-lasting energy resources.
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.
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