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.
학술저널
Full-text
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
Reliable charging infrastructure is an essential element to transform the current fossil fuel-centered automobile market into electric vehicles. In Korea, the supply level of public charging infrastructure is better than that of other countries, but the residential charging infrastructure is hard to expand due to the domestic characteristics. Therefore, in order to meet the electric vehicle era in the future, charging infrastructure supply strategies suitable for the domestic situation should be prepared. This study analyzed the charging patterns of electric vehicle drivers as essential data necessary for future charging infrastructure plans and decision-making on the supply of charging facilities. This study utilized the data of one-week charging events survey of 297 electric car drivers conducted in 2021, and the Latent Class Analysis was applied to identify the charging pattern of individual driver. As a result, the charging patterns of electric car drivers were classified into four types: Mixed & Slow 69.3%, Home & Slow 16.5%, Public-centric 8.2%, and Work & Slow 6.1%. As a result of analyzing the predictive variables of the charging pattern through multi-logit analysis, accessibility by charging infrastructure type and preference by type of charging infrastructure were found to be statistically significant affecting factors for all charging patterns. For some classes of charging pattern, annual driving mileage and parking conditions at home were also found to have a significant effect.
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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