Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet |
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| Affiliation: | 1. School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI 48109-1041, United States;2. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, United States;3. School of Environmental and Safety Engineering, Qingdao University of Science & Technology, Qingdao, China;4. Department of Industrial Engineering, De La Salle University, Manila, Philippines;5. Energy Research Institute, Shanghai Jiao Tong University, Shanghai, China;1. Department of Civil Engineering, University of Coimbra, Rua Luis dos Reis Santos, room SA3.4, 3030-788 Coimbra, Portugal;2. Department of Transport and Planning, Delft University of Technology, Stevinweg 1, 2628 CN Delft, Netherlands;3. Department of Mathematics, University of Coimbra, 3001-454 Coimbra, Portugal;1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;2. Harvard Law School, Harvard University, Cambridge, MA, 02142, USA;3. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA;1. Department of Industrial Engineering, Tsinghua University, Beijing 100084, PR China;2. Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, United States;3. School of Management and Engineering, Nanjing University, Nanjing 210093, PR China;1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing, 100191, China;2. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China;3. National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology, Beihang University, Beijing, 100191, China;1. Institute of Transportation Studies, Department of Civil and Environmental Engineering, University of California, Irvine, CA, United States;2. Department of Civil Engineering, Ryerson University, Toronto, Ontario, Canada;3. Department of National Transport Strategy Planning, The Korea Transport Institute, Gyeonggi-do, Republic of Korea |
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| Abstract: | Charging infrastructure is critical to the development of electric vehicle (EV) system. While many countries have implemented great policy efforts to promote EVs, how to build charging infrastructure to maximize overall travel electrification given how people travel has not been well studied. Mismatch of demand and infrastructure can lead to under-utilized charging stations, wasting public resources. Estimating charging demand has been challenging due to lack of realistic vehicle travel data. Public charging is different from refueling from two aspects: required time and home-charging possibility. As a result, traditional approaches for refueling demand estimation (e.g. traffic flow and vehicle ownership density) do not necessarily represent public charging demand. This research uses large-scale trajectory data of 11,880 taxis in Beijing as a case study to evaluate how travel patterns mined from big-data can inform public charging infrastructure development. Although this study assumes charging stations to be dedicated to a fleet of PHEV taxis which may not fully represent the real-world situation, the methodological framework can be used to analyze private vehicle trajectory data as well to improve our understanding of charging demand for electrified private fleet. Our results show that (1) collective vehicle parking “hotspots” are good indicators for charging demand; (2) charging stations sited using travel patterns can improve electrification rate and reduce gasoline consumption; (3) with current grid mix, emissions of CO2, PM, SO2, and NOx will increase with taxi electrification; and (4) power demand for public taxi charging has peak load around noon, overlapping with Beijing’s summer peak power. |
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| Keywords: | Electric vehicle Charging stations Travel pattern GPS data Transportation emission Grid impact |
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