Harnessing big data for estimating the energy consumption and driving range of electric vehicles

Fetene, Gebeyehu M., Kaplan, Sigal, Mabit, Stefan L., Jensen, Anders F. and Prato, Carlo Giacomo (2016). Harnessing big data for estimating the energy consumption and driving range of electric vehicles. In: Transportation Research Board (TRB) 95th Annual Meeting. Transportation Research Board (TRB) 95th Annual Meeting, Washington, DC, United States, (). 10-14 January 2016.

Author Fetene, Gebeyehu M.
Kaplan, Sigal
Mabit, Stefan L.
Jensen, Anders F.
Prato, Carlo Giacomo
Title of paper Harnessing big data for estimating the energy consumption and driving range of electric vehicles
Conference name Transportation Research Board (TRB) 95th Annual Meeting
Conference location Washington, DC, United States
Conference dates 10-14 January 2016
Proceedings title Transportation Research Board (TRB) 95th Annual Meeting
Publication Year 2016
Sub-type Fully published paper
Total pages 19
Language eng
Abstract/Summary Analyzing the factors that affect the energy efficiency of vehicles is crucial to the overall improvement of the environmental efficiency of the transport sector, one of the top polluting sectors at the global level. This study analyzes the energy consumption rate (ECR) and driving range (DR) of battery electric vehicles (BEVs) and provides insight into the factors that affect their energy consumption by harnessing big data from real-world driving. The analysis relied on four data sources: (i) driving patterns collected from 741 drivers over a two-year period; (ii) drivers’ characteristics; (iii) road type; (iv) weather conditions. The results of the analysis measure the mean ECR of BEVs at 0.183 kWh/km, underline a 34% increase in ECR and a 25% decrease in DR in the winter with respect to the summer, and suggest the electricity tariff for BEVs to be cost efficient with respect to conventional ones. Moreover, the results of the analysis show that driving speed, acceleration and temperature have non-linear effects on the ECR, while season and precipitation level have a strong linear effect. The econometric model of the ECR of BEVs suggests that the optimal driving speed is between 45 and 56 km/h and the ideal temperature from an energy efficiency perspective is 14 °C. Clearly, the performance of BEVs highly depends on the driving environment, the driving patterns, and the weather conditions, and the findings from this study enlighten the consumers to be more informed and manufacturers to be more aware of the actual utilization of BEVs.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Transportation Research Board Annual Meeting 2016 Paper #16-2689

Document type: Conference Paper
Collection: School of Civil Engineering Publications
 
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Created: Mon, 18 Apr 2016, 18:43:32 EST by Carlo Prato on behalf of School of Civil Engineering