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Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles
•Framework for optimal positioning of wireless charging facility for BEV.•Consider both system level social cost and energy consumed.•Introduce an efficient algorithm for solving black-box objective function.•Present a case-study using a County network. Dynamic wireless charging (DWC) offers a plaus...
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Published in: | Transportation research. Part D, Transport and environment Transport and environment, 2020-08, Vol.85, p.102385, Article 102385 |
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container_title | Transportation research. Part D, Transport and environment |
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creator | Ngo, Huan Kumar, Amit Mishra, Sabyasachee |
description | •Framework for optimal positioning of wireless charging facility for BEV.•Consider both system level social cost and energy consumed.•Introduce an efficient algorithm for solving black-box objective function.•Present a case-study using a County network.
Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy. |
doi_str_mv | 10.1016/j.trd.2020.102385 |
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Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy.</description><identifier>ISSN: 1361-9209</identifier><identifier>EISSN: 1879-2340</identifier><identifier>DOI: 10.1016/j.trd.2020.102385</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Battery electric vehicle ; Dynamic wireless charging ; Equity in resource distribution ; Travel time</subject><ispartof>Transportation research. Part D, Transport and environment, 2020-08, Vol.85, p.102385, Article 102385</ispartof><rights>2020 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-b50166b9e2d079a43a7653606d01f1707f8b4308b84bc6db088889ee183a5b383</citedby><cites>FETCH-LOGICAL-c358t-b50166b9e2d079a43a7653606d01f1707f8b4308b84bc6db088889ee183a5b383</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Ngo, Huan</creatorcontrib><creatorcontrib>Kumar, Amit</creatorcontrib><creatorcontrib>Mishra, Sabyasachee</creatorcontrib><title>Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles</title><title>Transportation research. Part D, Transport and environment</title><description>•Framework for optimal positioning of wireless charging facility for BEV.•Consider both system level social cost and energy consumed.•Introduce an efficient algorithm for solving black-box objective function.•Present a case-study using a County network.
Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy.</description><subject>Battery electric vehicle</subject><subject>Dynamic wireless charging</subject><subject>Equity in resource distribution</subject><subject>Travel time</subject><issn>1361-9209</issn><issn>1879-2340</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kN1KAzEQhYMoWKsP4F1eYGuy2WSzeCXFPyj0Rq9D_rZNbTdlkrb07c1Sr52bmcNwDjMfQo-UzCih4mkzy-BmNalHXTPJr9CEyrarataQ6zIzQauuJt0tuktpQwjhnIsJist9Dju9xfuYQg5xCMMKxx6786B3weJTAL_1KWG71rAal2HoQacMB5sP4IvEGkPUDg8-nyL84D4CNjpnD2dcvDZDyTn6dbAl6B7d9Hqb_MNfn6Lvt9ev-Ue1WL5_zl8WlWVc5srw8pQwna8daTvdMN0KzgQRjtCetqTtpWkYkUY2xgpniCzVeU8l09wwyaaIXnItxJTA92oP5U84K0rUSExtVCGmRmLqQqx4ni8eXw47Bg8q2eAH612BYLNyMfzj_gUV9XW7</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Ngo, Huan</creator><creator>Kumar, Amit</creator><creator>Mishra, Sabyasachee</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20200801</creationdate><title>Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles</title><author>Ngo, Huan ; Kumar, Amit ; Mishra, Sabyasachee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c358t-b50166b9e2d079a43a7653606d01f1707f8b4308b84bc6db088889ee183a5b383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Battery electric vehicle</topic><topic>Dynamic wireless charging</topic><topic>Equity in resource distribution</topic><topic>Travel time</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ngo, Huan</creatorcontrib><creatorcontrib>Kumar, Amit</creatorcontrib><creatorcontrib>Mishra, Sabyasachee</creatorcontrib><collection>CrossRef</collection><jtitle>Transportation research. Part D, Transport and environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ngo, Huan</au><au>Kumar, Amit</au><au>Mishra, Sabyasachee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles</atitle><jtitle>Transportation research. Part D, Transport and environment</jtitle><date>2020-08-01</date><risdate>2020</risdate><volume>85</volume><spage>102385</spage><pages>102385-</pages><artnum>102385</artnum><issn>1361-9209</issn><eissn>1879-2340</eissn><abstract>•Framework for optimal positioning of wireless charging facility for BEV.•Consider both system level social cost and energy consumed.•Introduce an efficient algorithm for solving black-box objective function.•Present a case-study using a County network.
Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.trd.2020.102385</doi></addata></record> |
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subjects | Battery electric vehicle Dynamic wireless charging Equity in resource distribution Travel time |
title | Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles |
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