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论文类型：期刊论文

发表时间：2016-12-01

发表刊物：APPLIED ENERGY

收录刊物：SCIE、EI、Scopus

卷号：183

页面范围：1351-1360

ISSN号：0306-2619

关键字：Electric vehicles; Energy efficiency; Multilevel analysis; Heterogeneity

摘要：To improve the accuracy of estimation of the energy consumption of electric vehicles (EVs) and to enable the alleviation of range anxiety through the introduction of EV charging stations at suitable locations for the near future, multilevel mixed-effects linear regression models were used in this study to estimate the actual energy efficiency of EVs. The impacts of the heterogeneity in driving behaviour among various road environments and traffic conditions on EV energy efficiency were extracted from long-term daily trip based energy consumption data, which were collected over 12 months from 68 in-use EVs in Aichi Prefecture in Japan. Considering the variations in energy efficiency associated with different types of EV ownership, different external environments, and different driving habits, a two-level random intercept model, three two-level mixed-effects models, and two three-level mixed-effects models were developed and compared. The most reasonable nesting structure was determined by comparing the models, which were designed with different nesting structures and different random variance component specifications, thereby revealing the potential correlations and non-constant variability of the energy consumption per kilometre (ECPK) and improving the estimation accuracy by 7.5%. (C) 2016 Elsevier Ltd. All rights reserved.