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Title: Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint

Abstract

A data-driven technique for estimation of energy requirements for a proposed vehicle trip has been developed. Based on over 700,000 miles of driving data, the technique has been applied to generate a model that estimates trip energy requirements. The model uses a novel binning approach to categorize driving by road type, traffic conditions, and driving profile. The trip-level energy estimations can easily be aggregated to any higher-level transportation system network desired. The model has been tested and validated on the Austin, Texas, data set used to build this model. Ground-truth energy consumption for the data set was obtained from Future Automotive Systems Technology Simulator (FASTSim) vehicle simulation results. The energy estimation model has demonstrated 12.1 percent normalized total absolute error. The energy estimation from the model can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations, to reduce energy consumption. The model can also be used to determine more accurate energy consumption of regional or national transportation networks if trip origin and destinations are known. Additionally, this method allows the estimation tool to be tuned to a specific driver or vehicle type.

Authors:
 [1];  [1];  [1];  [1];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Metropia, Inc.
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1393793
Report Number(s):
NREL/CP-5400-69121
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at ITS World Congress 2017, 29 October - 2 November 2017, Montreal, Canada
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; energy estimation; drive cycles; green routing; GPS

Citation Formats

Holden, Jacob, Wood, Eric W, Zhu, Lei, Gonder, Jeffrey D, and Tian, Ye. Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint. United States: N. p., 2017. Web.
Holden, Jacob, Wood, Eric W, Zhu, Lei, Gonder, Jeffrey D, & Tian, Ye. Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint. United States.
Holden, Jacob, Wood, Eric W, Zhu, Lei, Gonder, Jeffrey D, and Tian, Ye. 2017. "Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1393793.
@article{osti_1393793,
title = {Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint},
author = {Holden, Jacob and Wood, Eric W and Zhu, Lei and Gonder, Jeffrey D and Tian, Ye},
abstractNote = {A data-driven technique for estimation of energy requirements for a proposed vehicle trip has been developed. Based on over 700,000 miles of driving data, the technique has been applied to generate a model that estimates trip energy requirements. The model uses a novel binning approach to categorize driving by road type, traffic conditions, and driving profile. The trip-level energy estimations can easily be aggregated to any higher-level transportation system network desired. The model has been tested and validated on the Austin, Texas, data set used to build this model. Ground-truth energy consumption for the data set was obtained from Future Automotive Systems Technology Simulator (FASTSim) vehicle simulation results. The energy estimation model has demonstrated 12.1 percent normalized total absolute error. The energy estimation from the model can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations, to reduce energy consumption. The model can also be used to determine more accurate energy consumption of regional or national transportation networks if trip origin and destinations are known. Additionally, this method allows the estimation tool to be tuned to a specific driver or vehicle type.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 9
}

Conference:
Other availability
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