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• W2990133168 abstract "Abstract Cities are keen to encourage the shift to electric vehicles, its major promise being a cut in air pollution. With the recent advances in battery technology and the resulting decrease in the charging times, a strong focus is placed on the range of electric vehicles. Motivated by these developments, a data-driven framework is introduced, that analyses vehicle trajectories and driving patterns in order to assess the potential electrification of vehicles, taking into account available charging infrastructure. As a case study, this work uses trajectory data from a fleet of taxis operating in Hamburg over a one-month period. The proposed data-driven assessment methodology grounds on a battery and charging behaviour model that takes into account the mobility patterns of network constrained vehicle trajectories. The distributions of electric driving share are computed based on a battery model with a maximum battery capacity of 30 kWh. While the outcomes of this simulation are based on different charging behaviour scenarios, all result in high shares exceeding 89 % of vehicles that could drive fully electric. In addition, the influence of battery sizes on vehicle electrification is examined. When collectively considered, the results presented in this study underline the ability of datadriven approaches to assess the electrification of vehicles. Furthermore, strong arguments for the adoption of electric vehicles for certain individuals or companies (e.g. fleet vehicles) is provided." @default.
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• W2990133168 date "2019-01-01" @default.
• W2990133168 modified "2023-10-18" @default.
• W2990133168 title "Quantifying the potential of electrification with large-scale vehicle trajectory data" @default.
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• W2990133168 doi "https://doi.org/10.1016/j.trpro.2019.09.103" @default.
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