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Allocation optimisation of rapid charging stations in large urban areas to support fully electric taxi fleets

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  • Cilio, Luca
  • Babacan, Oytun

Abstract

Electric taxis can help reducing air pollution in crowded urban areas but their city-wide operation requires a distributed rapid charging infrastructure. Building such rapid charging networks is currently capital intensive and therefore requires careful planning. Here, we propose a novel data-driven framework for deploying suitable rapid charging infrastructures in large urban areas for electric taxis. This framework combines an iterative clustering technique with a modified numerical optimisation method to determine the smallest feasible infrastructure for a level of charging availability that ensures uninterrupted electric taxi service. We provide a case study for Istanbul using real-time global positioning data from fossil-fuel taxis currently operational in the city. This case study tests the performance of the proposed infrastructure, determined by the framework, by simulating a taxi fleet of the same size as the one currently operating in Istanbul. Our results show that a sufficient charging infrastructure to serve a fully electric taxi fleet of 17,395 vehicles in a large city like Istanbul should consist of around 1,363–1,834 charging stations depending on the roll-out strategy. In the most suitable case, each charging station on average provides a daily amount of energy of 449.61 kWh and usually serves about 20 electric taxis per day. Furthermore, we observe that infrastructures with less than 1,300 charging stations would result in significant shortages of charging availability and adversely impact a reliable electric taxi service operation. While exact numbers of required charging stations would vary depending on the city characteristics and fleet size, the roll-out strategies, in addition to the underlying feasibility analysis presented here, would support transport authorities and other decision makers in shaping an appropriate urban transition strategy that accommodates electric taxi services.

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  • Cilio, Luca & Babacan, Oytun, 2021. "Allocation optimisation of rapid charging stations in large urban areas to support fully electric taxi fleets," Applied Energy, Elsevier, vol. 295(C).
    • Handle: RePEc:eee:appene:v:295:y:2021:i:c:s0306261921005249
    DOI: 10.1016/j.apenergy.2021.117072
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    References listed on IDEAS

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    2. Kinsella, L. & Stefaniec, A. & Foley, A. & Caulfield, B., 2023. "Pathways to decarbonising the transport sector: The impacts of electrifying taxi fleets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    3. Peng, Zhenhan & Wang, Zhuowei & Wang, Shiqi & Chen, Anthony & Zhuge, Chengxiang, 2024. "Fuel and infrastructure options for electrifying public transit: A data-driven micro-simulation approach," Applied Energy, Elsevier, vol. 369(C).
    4. Pichamon Keawthong & Veera Muangsin & Chupun Gowanit, 2022. "Location Selection of Charging Stations for Electric Taxis: A Bangkok Case," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    5. Faustino, Fausta J. & Lopes, José Calixto & Melo, Joel D. & Sousa, Thales & Padilha-Feltrin, Antonio & Brito, José A.S. & Garcia, Claudio O., 2023. "Identifying charging zones to allocate public charging stations for electric vehicles," Energy, Elsevier, vol. 283(C).
    6. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

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