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Title: Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration

Abstract

This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle(EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. This model aids in analyzing EV energy cost and describing uncertainties under variable traffic and ambient temperature conditions. According to this energy consumption model, an optimal eco-routing and charging decision making framework is designed for autonomous EV fleet. This optimization framework can improve the capability of autonomous EV's energy management and help to find their minimum energy cost routes with consideration of charging demand and travel time requirements. Based on these promising models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, there may be more than 20% difference of overall energy cost and 60% difference of charging demand in a specific charging station between low and high ambient temperature of a month. All these studies will help to construct sustainable infrastructure for autonomous electric vehicle fleet management.

Authors:
ORCiD logo [1];  [1];  [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1477237
Report Number(s):
INL/JOU-17-43359-Rev000
Journal ID: ISSN 0968-090X
Grant/Contract Number:  
AC07-05ID14517
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 89; Journal Issue: C; Journal ID: ISSN 0968-090X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Energy Impact Evaluation; Data-Driven Method; Energy Consumption Model; Eco-Routing; Charging Decision Making; Autonomous Electric Vehicle Fleet

Citation Formats

Yi, Zonggen, Smart, John, and Shirk, Matthew. Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration. United States: N. p., 2018. Web. doi:10.1016/j.trc.2018.02.018.
Yi, Zonggen, Smart, John, & Shirk, Matthew. Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration. United States. doi:10.1016/j.trc.2018.02.018.
Yi, Zonggen, Smart, John, and Shirk, Matthew. Sun . "Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration". United States. doi:10.1016/j.trc.2018.02.018. https://www.osti.gov/servlets/purl/1477237.
@article{osti_1477237,
title = {Energy impact evaluation for eco-routing and charging of autonomous electric vehicle fleet: Ambient temperature consideration},
author = {Yi, Zonggen and Smart, John and Shirk, Matthew},
abstractNote = {This paper studies the heterogeneous energy cost and charging demand impact of autonomous electric vehicle(EV) fleet under different ambient temperature. A data-driven method is introduced to formulate a two-dimensional grid stochastic energy consumption model for electric vehicles. This model aids in analyzing EV energy cost and describing uncertainties under variable traffic and ambient temperature conditions. According to this energy consumption model, an optimal eco-routing and charging decision making framework is designed for autonomous EV fleet. This optimization framework can improve the capability of autonomous EV's energy management and help to find their minimum energy cost routes with consideration of charging demand and travel time requirements. Based on these promising models and technologies, comprehensive case studies are performed on a data-driven simulated transportation network in New York City. Detailed results show us the heterogeneous energy impact and charging demand under different ambient temperature. By giving the same travel demand and charging station information, there may be more than 20% difference of overall energy cost and 60% difference of charging demand in a specific charging station between low and high ambient temperature of a month. All these studies will help to construct sustainable infrastructure for autonomous electric vehicle fleet management.},
doi = {10.1016/j.trc.2018.02.018},
journal = {Transportation Research Part C: Emerging Technologies},
issn = {0968-090X},
number = C,
volume = 89,
place = {United States},
year = {2018},
month = {3}
}

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