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
Abstract The transition to battery electric buses (BEBs) offers a significant opportunity to reduce greenhouse gas (GHG) emissions in public transit. However, the limited driving range of BEBs presents operational challenges, making accurate energy demand prediction essential for effective deployment. Despite advances in machine learning and data-driven modeling, an integrated framework for real-world BEB energy demand prediction remains underdeveloped. Most existing research in this domain relies heavily on simulated or controlled datasets, limiting practical applicability. This study addresses this gap by presenting a comprehensive approach to predicting the energy demands of a BEB fleet under actual service conditions, grounded in real-world operational data collected from the Toronto Transit Commission’s (TTC) BEB trial, one of the largest of its kind in North America. At the core of this approach is a novel data processing framework specifically designed for streaming high-resolution vehicle telematics data, which integrates diverse contextual sources such as weather conditions, route topology, passenger loads, and bus schedules. This integrated framework enables the construction of a large-scale BEB dataset derived from in-service operational data of the TTC’s BEB fleet, encompassing 149,813 hours of driving and 2.56 million kilometers traveled. The dataset is leveraged to train and evaluate several machine learning models to predict energy demands along TTC routes. Results demonstrate that the best-performing model achieves a 38% reduction in mean absolute error compared to a baseline method and explains 87% of the variance in net energy demand. Additionally, an analysis of seasonal effects reveals heightened prediction challenges during colder months, driven by increased variability in energy consumption across different BEB makes and models. Finally, a physics-informed hybrid modeling approach is proposed, which integrates energy estimates from vehicle longitudinal dynamics into the data-driven pipeline, yielding further improvements in prediction accuracy and underscoring the value of domain knowledge in machine learning applications for transit.
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
