Battery electric vehicle usage pattern analysis driven by massive real-world data

Dingsong Cui, Zhenpo Wang, Peng Liu*, Shuo Wang, Zhaosheng Zhang, David G. Dorrell, Xiaohui Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)

Abstract

Electric vehicles (EVs) are playing a key role in supporting transportation electrification and reducing air pollution and greenhouse gas emissions. The increased number of EVs may also bring about some issues concerning energy system structure optimization and efficiency enhancement. User behavior analysis and simulation is an important method to solve these issues. A stochastic model for describing the usage of vehicle is essential to handle simulation models and behavior models. Therefore, a more comprehensive understanding of EV usage patterns is necessary for the model establishment. The paper focuses on the 2,047,222 charging events and 8,382,032 travel events collected from 26,606 battery electric vehicles operating in Beijing, China, in 2018, based on the open lab of National Big Data Alliance of New Energy Vehicles. With the large-scale data resource rather than limited samples, we provide some robust statistical results and some multi-dimensional comparative analysis in the paper, which can be applied in large-scale deployment environments and large population cities. The results can also provide information for charging infrastructures construction, gird management, vehicle charging scheduling, and so forth in Beijing and even other metropolises with similar situations.

Original languageEnglish
Article number123837
JournalEnergy
Volume250
DOIs
Publication statusPublished - 1 Jul 2022

Keywords

  • Battery electric vehicle
  • Energy demand
  • Massive real-world data
  • Transportation electrification
  • Usage patterns

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