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| Mouftah, Hussein T. |
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| Dugay, Fabrice |
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| Rettenmeier, Max |
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| Tomasch, Ernst | Graz |
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| Cornaggia, Greta |
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| Palacios-Navarro, Guillermo |
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| Uspenskyi, Borys V. |
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| Khan, Baseem |
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| Fediai, Natalia |
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| Derakhshan, Shadi |
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| Somers, Bart | Eindhoven |
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| Anvari, B. |
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| Kraushaar, Sabine | Vienna |
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| Kehlbacher, Ariane |
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| Das, Raj |
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| Werbińska-Wojciechowska, Sylwia |
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| Brillinger, Markus |
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| Eskandari, Aref |
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| Gulliver, J. |
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| Loft, Shayne |
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| Kud, Bartosz |
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| Matijošius, Jonas | Vilnius |
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| Piontek, Dennis |
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| Kene, Raymond O. |
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| Barbosa, Juliana |
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Jensen, Anders Fjendbo
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (48/48 displayed)
- 2025Demand-side challenges and research needs on the road to 100% zero-emission vehicle salescitations
- 2025The Overlooked Role of Roadworks in Micromobility’s Accessibility
- 2025Context-aware Bayesian mixed multinomial logit modelcitations
- 2024Transportforsker
- 2024Comparative modeling of risk factors for near-crashes from crowdsourced bicycle airbag helmet data and crashes from conventional police datacitations
- 2024Riding smooth: A cost-benefit assessment of surface quality on Copenhagen’s bicycle networkcitations
- 2023Analysis of cycling accessibility using detour ratios – A large-scale study based on crowdsourced GPS datacitations
- 2023A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS datasetcitations
- 2023How should we develop the charging network? User and industry expectations
- 2023Can crowdsourced large-scale near-crash data replace crash data? A comparison of models using both sources
- 2023Empirical analysis of cycling distances in three of Europe’s most bicycle-friendly regions within an accessibility frameworkcitations
- 2022Effects of autonomous first- and last mile transport in the transport chaincitations
- 2022Editorial: Longer Distance Cycling: Roles, Requirements and Impactscitations
- 2022Facilitating bicycle commuting beyond short distances: insights from existing literaturecitations
- 2022User preferences for EV charging, pricing schemes, and charging infrastructurecitations
- 2021Cost-benefit of bicycle infrastructure with e-bikes and cycle superhighwayscitations
- 2021Demand for plug-in electric vehicles across segments in the future vehicle marketcitations
- 202114 forskere: Videnscenter for cyklisme skal ikke høre under Vejdirektoratet
- 2021Battery electric vehicle adoption in Denmark and Sweden: Recent changes, related factors and policy implicationscitations
- 2020Understanding car sharing preferences and mode substitution patterns: A stated preference experimentcitations
- 2020A route choice model for capturing driver preferences when driving electric and conventional vehiclescitations
- 2020Effekt- og brugerundersøgelse af E-bybiler i Region Hovedstaden
- 2020Analyse af indfasning af elbiler: SP metode og model
- 2019Active transport modes
- 2019Willingness to pay for electric vehicles and vehicle-to-grid applications: A Nordic choice experimentcitations
- 2019A disaggregate freight transport chain choice model for Europecitations
- 2019Using crowd source data in bicycle route choice modeling
- 2018Modellering af cykeltrafik
- 2018A dynamic approach to model the impact of imitation and experience
- 2018Factors of electric vehicle adoption: A comparison of conventional and electric car users based on an extended theory of planned behaviorcitations
- 2018A Joint Route Choice Model for Capturing Preferences of Electric and Conventional Car Drivers
- 2017The use of electric vehicles: A case study on adding an electric car to a householdcitations
- 2017Predicting the Potential Market for Electric Vehiclescitations
- 2017Harnessing big data for estimating the energy consumption and driving range of electric vehiclescitations
- 2017Harnessing big data for estimating the energy consumption and driving range of electric vehiclescitations
- 2017Actual preferences for EV households in Denmark and Sweden
- 2017A Joint Route Choice Model for Electric and Conventional Car Users
- 2016Harnessing Big-Data for Estimating the Energy Consumption and Driving Range of Electric Vehicles
- 2016Harnessing Big-Data for Estimating the Energy Consumption and Driving Range of Electric Vehicles
- 2015Bounded Rational Choice Behaviour: Applications in Transportcitations
- 2015På cykel til DTU
- 2015The effect of attitudes on reference-dependent preferences: Estimation and validation for the case of alternative-fuel vehiclescitations
- 2014Assesing the Impact of Direct Experience on Individual Preferences and Attitudes for Electric Vehicles
- 2014Assessing the Impact of Direct Experience on Individual Preferences and Attitudes for Electric Vehicles
- 2014A long panel survey to elicit variation in preferences and attitudes in the choice of electric vehiclescitations
- 2014Corrigendum to “On the stability of preferences and attitudes before and after experiencing an electric vehicle” [Transport. Res. Part D 25C (2013) 24–32]citations
- 2013On the stability of preferences and attitudes before and after experiencing an electric vehiclecitations
- 2011Vil bilisterne købe elbiler?
Places of action
| Organizations | Location | People |
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conferencepaper
Harnessing Big-Data for Estimating the Energy Consumption and Driving Range of Electric Vehicles
Abstract
This study analyses the driving range and investigates the factors affecting the energy consumption rate of fully-battery electric vehicles under real-world driving patterns accounting for weather condition, drivers’ characteristics, and road characteristics. Four data sources are used: (i) up to six months driving pattern data collected from 741 drivers, (ii) drivers’ characteristics; (iii) road characteristics; (iv) weather data. We found that the real-world driving range of BEVs is highly sensitive to driving pattern and weather variables. The most important determinants of energy efficiency found to be driving patterns (acceleration and speed, both non-linearly) followed by seasonal variation (a winter dummy), temperature (non-linearly) and precipitation. Mean ECR is higher by about 34 % and the driving range is lower by about 25 % in winter than in summer. A fixed-effects econometrics model used in this paper predicts that the energy saving speed of driving is between 45 and 56 km/h. In addition to the contribution to the literature about energy efficiency of electric vehicles, the findings from this study enlightens consumers to choose appropriate cars that suit their travel demand under the driving environment they live in, to know about energy saving patterns of drive, and to reduce driving range anxiety problem.
Topics
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