Predictive modeling of energy consumption and greenhouse gas emissions from autonomous electric vehicle operations

Cheng Zhang, Fan Yang, Xinyou Ke, Zhifeng Liu and Chris Yuan

Applied Energy, 2019, vol. 254, issue C

Abstract: Autonomous electric vehicles have attracted enormous interests as an effective way to significantly improve urban transportation efficiency, reduce commute cost and the corresponding environmental burden. This work proposed a multiphysics energy model to quantify the energy consumption and greenhouse gas emissions from an autonomous electric vehicle based on vehicle dynamics and the vehicle system energy demand. A case study is conducted on a mid-size autonomous electric vehicles taxi operating in New York City based on possible driving data and scenarios. It is found that the monthly average unit energy consumption for the autonomous electric vehicle ranges from 325 to 397 Wh km−1, and the greenhouse gas emissions is 6.5% more from an autonomous electric vehicle with a driver than that without a driver. The study provides a physical approach for quantifying the energy consumption and greenhouse gas emissions from an autonomous electric vehicle, and can support the sustainable development and deployment of autonomous electric vehicle technologies in future.

Keywords: Autonomous electric vehicles; Energy consumption; Greenhouse gas emissions; Taxi driving mode; Predictive modeling (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.apenergy.2019.113597

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