Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

Mohamed, Ahmed A. S.; Zhu, Lei; Meintz, Andrew L; Wood, Eric W

doi:10.1109/TIA.2019.2958566

Title: Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

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Abstract

Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementingmore »« less

Authors:

^[1];

^[1]

National Renewable Energy Lab. (NREL), Golden, CO (United States). Center for Integrated Mobility Sciences

Publication Date:: Mon Dec 09 00:00:00 EST 2019

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:: 1583091

Report Number(s):: NREL/JA-5400-74341
Journal ID: ISSN 0093-9994; MainId:18226;UUID:81726684-13a7-e911-9c24-ac162d87dfe5;MainAdminID:6704

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Journal Article: Accepted Manuscript

Journal Name:: IEEE Transactions on Industry Applications

Additional Journal Information:: Journal Volume: 56; Journal Issue: 2; Journal ID: ISSN 0093-9994

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 33 ADVANCED PROPULSION SYSTEMS; autonomous electric shuttles; dynamic wireless charging; fixed route; optimization; wireless power transfer; WPT

Citation Formats


                    Mohamed, Ahmed A. S., Zhu, Lei, Meintz, Andrew L, and Wood, Eric W. Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina.  United States: N. p., 2019. 
        Web.  doi:10.1109/TIA.2019.2958566.

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                    Mohamed, Ahmed A. S., Zhu, Lei, Meintz, Andrew L, & Wood, Eric W. Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina.  United States.  https://doi.org/10.1109/TIA.2019.2958566

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                    Mohamed, Ahmed A. S., Zhu, Lei, Meintz, Andrew L, and Wood, Eric W. 2019.  
        "Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina".  United States.  https://doi.org/10.1109/TIA.2019.2958566.  https://www.osti.gov/servlets/purl/1583091.

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@article{osti_1583091,

  title        = {Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina},

  author       = {Mohamed, Ahmed A. S. and Zhu, Lei and Meintz, Andrew L and Wood, Eric W},

  abstractNote = {Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementing high-power (100 kW) wireless charger at a few designated stops for fixed-route SAESs with the proper track length allows the vehicles to realize charge-sustaining operation, infinite range, and zero recharge downtime, with a significant reduction in the on-board battery (36%) and road coverage (69%), at minimum cost.},

  doi          = {10.1109/TIA.2019.2958566},

  url          = {https://www.osti.gov/biblio/1583091},
  journal      = {IEEE Transactions on Industry Applications},

  issn         = {0093-9994},

  number       = 2,

  volume       = 56,

  place        = {United States},

  year         = {Mon Dec 09 00:00:00 EST 2019},

  month        = {Mon Dec 09 00:00:00 EST 2019}

}

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