Matches in SemOpenAlex for { <https://semopenalex.org/work/W2016087887> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W2016087887 endingPage "67" @default.
- W2016087887 startingPage "52" @default.
- W2016087887 abstract "Abstract Use of electric vehicles (EVs) has been viewed by many as a way to significantly reduce oil dependence, operate vehicles more efficiently, and reduce carbon emissions. Due to the potential benefits of EVs, the federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve efficiency for EV transportation system? This research takes the first step in tackling this challenge by addressing a fundamental issue, i.e. how to measure and estimate EVs’ energy consumption. In detail, this paper first presents a system which can collect in-use EV data and vehicle driving data. This system then has been installed in an EV conversion vehicle built in this research as a test vehicle. Approximately 5 months of EV data have been collected and these data have been used to analyze both EV performance and driver behaviors. The analysis shows that the EV is more efficient when driving on in-city routes than driving on freeway routes. Further investigation of this particular EV driver’s route choice behavior indicates that the EV user tries to balance the trade-off between travel time and energy consumption. Although more data are needed in order to generalize this finding, this observation could be important and might bring changes to the traffic assignment for future transportation system with a significant share of EVs. Additionally, this research analyzes the relationships among the EV’s power, the vehicle’s velocity, acceleration, and the roadway grade. Based on the analysis results, this paper further proposes an analytical EV power estimation model. The evaluation results using the test vehicle show that the proposed model can successfully estimate EV’s instantaneous power and trip energy consumption. Future research will focus on applying the proposed EV power estimation model to improve EVs’ energy efficiency." @default.
- W2016087887 created "2016-06-24" @default.
- W2016087887 creator A5039406844 @default.
- W2016087887 creator A5050746498 @default.
- W2016087887 creator A5075274745 @default.
- W2016087887 creator A5088309790 @default.
- W2016087887 date "2015-01-01" @default.
- W2016087887 modified "2023-10-10" @default.
- W2016087887 title "Electric vehicles’ energy consumption measurement and estimation" @default.
- W2016087887 cites W1974624146 @default.
- W2016087887 cites W1982638855 @default.
- W2016087887 cites W1994996260 @default.
- W2016087887 cites W2002635343 @default.
- W2016087887 cites W2018996296 @default.
- W2016087887 cites W2081677832 @default.
- W2016087887 cites W2100508296 @default.
- W2016087887 cites W2118577580 @default.
- W2016087887 cites W2125684773 @default.
- W2016087887 cites W2131874922 @default.
- W2016087887 cites W2146363506 @default.
- W2016087887 cites W3101402193 @default.
- W2016087887 doi "https://doi.org/10.1016/j.trd.2014.10.007" @default.
- W2016087887 hasPublicationYear "2015" @default.
- W2016087887 type Work @default.
- W2016087887 sameAs 2016087887 @default.
- W2016087887 citedByCount "301" @default.
- W2016087887 countsByYear W20160878872015 @default.
- W2016087887 countsByYear W20160878872016 @default.
- W2016087887 countsByYear W20160878872017 @default.
- W2016087887 countsByYear W20160878872018 @default.
- W2016087887 countsByYear W20160878872019 @default.
- W2016087887 countsByYear W20160878872020 @default.
- W2016087887 countsByYear W20160878872021 @default.
- W2016087887 countsByYear W20160878872022 @default.
- W2016087887 countsByYear W20160878872023 @default.
- W2016087887 crossrefType "journal-article" @default.
- W2016087887 hasAuthorship W2016087887A5039406844 @default.
- W2016087887 hasAuthorship W2016087887A5050746498 @default.
- W2016087887 hasAuthorship W2016087887A5075274745 @default.
- W2016087887 hasAuthorship W2016087887A5088309790 @default.
- W2016087887 hasConcept C105795698 @default.
- W2016087887 hasConcept C119599485 @default.
- W2016087887 hasConcept C127413603 @default.
- W2016087887 hasConcept C144024400 @default.
- W2016087887 hasConcept C171146098 @default.
- W2016087887 hasConcept C186370098 @default.
- W2016087887 hasConcept C201995342 @default.
- W2016087887 hasConcept C2780165032 @default.
- W2016087887 hasConcept C30772137 @default.
- W2016087887 hasConcept C33923547 @default.
- W2016087887 hasConcept C36289849 @default.
- W2016087887 hasConcept C39432304 @default.
- W2016087887 hasConcept C41008148 @default.
- W2016087887 hasConcept C96250715 @default.
- W2016087887 hasConceptScore W2016087887C105795698 @default.
- W2016087887 hasConceptScore W2016087887C119599485 @default.
- W2016087887 hasConceptScore W2016087887C127413603 @default.
- W2016087887 hasConceptScore W2016087887C144024400 @default.
- W2016087887 hasConceptScore W2016087887C171146098 @default.
- W2016087887 hasConceptScore W2016087887C186370098 @default.
- W2016087887 hasConceptScore W2016087887C201995342 @default.
- W2016087887 hasConceptScore W2016087887C2780165032 @default.
- W2016087887 hasConceptScore W2016087887C30772137 @default.
- W2016087887 hasConceptScore W2016087887C33923547 @default.
- W2016087887 hasConceptScore W2016087887C36289849 @default.
- W2016087887 hasConceptScore W2016087887C39432304 @default.
- W2016087887 hasConceptScore W2016087887C41008148 @default.
- W2016087887 hasConceptScore W2016087887C96250715 @default.
- W2016087887 hasLocation W20160878871 @default.
- W2016087887 hasOpenAccess W2016087887 @default.
- W2016087887 hasPrimaryLocation W20160878871 @default.
- W2016087887 hasRelatedWork W102836821 @default.
- W2016087887 hasRelatedWork W1975629292 @default.
- W2016087887 hasRelatedWork W2113644930 @default.
- W2016087887 hasRelatedWork W2348507346 @default.
- W2016087887 hasRelatedWork W2365826228 @default.
- W2016087887 hasRelatedWork W2375480182 @default.
- W2016087887 hasRelatedWork W2376774548 @default.
- W2016087887 hasRelatedWork W2406747782 @default.
- W2016087887 hasRelatedWork W2502168120 @default.
- W2016087887 hasRelatedWork W2562918078 @default.
- W2016087887 hasVolume "34" @default.
- W2016087887 isParatext "false" @default.
- W2016087887 isRetracted "false" @default.
- W2016087887 magId "2016087887" @default.
- W2016087887 workType "article" @default.