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Title: Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint

Abstract

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any type of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results 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. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
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:
1421377
Report Number(s):
NREL/CP-5400-70512
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at 97th Annual Meeting of the Transportation Research Board, 7-11 January 2018, Washington, D. C.
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; 33 ADVANCED PROPULSION SYSTEMS; energy estimation; drive cycles; green routing; GPS trajectories; transportation data; trip energy

Citation Formats

Holden, Jacob, Van Til, Harrison J, Wood, Eric W, Gonder, Jeffrey D, and Zhu, Lei. Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint. United States: N. p., 2018. Web.
Holden, Jacob, Van Til, Harrison J, Wood, Eric W, Gonder, Jeffrey D, & Zhu, Lei. Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint. United States.
Holden, Jacob, Van Til, Harrison J, Wood, Eric W, Gonder, Jeffrey D, and Zhu, Lei. Fri . "Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1421377.
@article{osti_1421377,
title = {Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green Routing Applications: Preprint},
author = {Holden, Jacob and Van Til, Harrison J and Wood, Eric W and Gonder, Jeffrey D and Zhu, Lei},
abstractNote = {A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any type of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results 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. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Feb 09 00:00:00 EST 2018},
month = {Fri Feb 09 00:00:00 EST 2018}
}

Conference:
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