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Title: A real-time vehicle-specific eco-routing model for on-board navigation applications capturing transient vehicle behavior

Abstract

The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do notmore » vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.« less

Authors:
ORCiD logo [1];  [2];  [2]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States). Center for Sustainable Mobility
Publication Date:
Research Org.:
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1509856
Report Number(s):
DOE-VT-0008209-J02
Journal ID: ISSN 0968-090X
Grant/Contract Number:  
EE0008209
Resource Type:
Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 104; Journal Issue: C; Journal ID: ISSN 0968-090X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 54 ENVIRONMENTAL SCIENCES; 42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; Eco-routing, Microscopic Simulation, Real-time On-board Navigation, Intelligent Transportation Systems, Connected Vehicles, Energy Consumption, Greenhouse Gas Emissions, Route Choice

Citation Formats

Wang, Jinghui, Elbery, Ahmed, and Rakha, Hesham A. A real-time vehicle-specific eco-routing model for on-board navigation applications capturing transient vehicle behavior. United States: N. p., 2019. Web. doi:10.1016/j.trc.2019.04.017.
Wang, Jinghui, Elbery, Ahmed, & Rakha, Hesham A. A real-time vehicle-specific eco-routing model for on-board navigation applications capturing transient vehicle behavior. United States. doi:10.1016/j.trc.2019.04.017.
Wang, Jinghui, Elbery, Ahmed, and Rakha, Hesham A. Tue . "A real-time vehicle-specific eco-routing model for on-board navigation applications capturing transient vehicle behavior". United States. doi:10.1016/j.trc.2019.04.017.
@article{osti_1509856,
title = {A real-time vehicle-specific eco-routing model for on-board navigation applications capturing transient vehicle behavior},
author = {Wang, Jinghui and Elbery, Ahmed and Rakha, Hesham A.},
abstractNote = {The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do not vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.},
doi = {10.1016/j.trc.2019.04.017},
journal = {Transportation Research Part C: Emerging Technologies},
number = C,
volume = 104,
place = {United States},
year = {2019},
month = {4}
}

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