Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals
This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional v...
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Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2014-10, Vol.15 (5), p.1958-1968 |
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container_title | IEEE transactions on intelligent transportation systems |
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creator | Bashash, Saeid Fathy, Hosam K. |
description | This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations. |
doi_str_mv | 10.1109/TITS.2014.2308283 |
format | Article |
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The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2014.2308283</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Batteries ; Bidirectional ; Charging ; Electric batteries ; Electric utilities ; Electric vehicles ; Electricity ; Electricity pricing ; Energy loss ; Fuels ; Hybrid vehicles ; Integrated circuit modeling ; Mathematical models ; Optimization ; power demand ; Quadratic programming ; smart grids ; System-on-chip</subject><ispartof>IEEE transactions on intelligent transportation systems, 2014-10, Vol.15 (5), p.1958-1968</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Oct 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-9a7aa7ab0ee7370a7b50848ce8002419814854a6883dd16da762dfb3be17ec6b3</citedby><cites>FETCH-LOGICAL-c396t-9a7aa7ab0ee7370a7b50848ce8002419814854a6883dd16da762dfb3be17ec6b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6767057$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6767057$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bashash, Saeid</creatorcontrib><creatorcontrib>Fathy, Hosam K.</creatorcontrib><title>Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations.</description><subject>Batteries</subject><subject>Bidirectional</subject><subject>Charging</subject><subject>Electric batteries</subject><subject>Electric utilities</subject><subject>Electric vehicles</subject><subject>Electricity</subject><subject>Electricity pricing</subject><subject>Energy loss</subject><subject>Fuels</subject><subject>Hybrid vehicles</subject><subject>Integrated circuit modeling</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>power demand</subject><subject>Quadratic programming</subject><subject>smart grids</subject><subject>System-on-chip</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LxDAQhosouH78APES8OKlayZJk_Qoi7oLggtbvYa0ndYs3XZNuof-e1tWPQiBNwzPOwxPFN0AnQPQ9CFbZZs5oyDmjFPNND-JZpAkOqYU5On0ZyJOaULPo4sQtuNUJACzqFp0oY_f9r3b2YYsPq2vXVuTriLr5lDHq5Ysh9y7kjw1WPTeFeQDP13RYCDvbYmeZG6H8Yf1w1T7hVw_kPWYSDaubm0TrqKzagy8_snL6P35KVss49e3l9Xi8TUueCr7OLXKji-niIoralWeUC10gZpSJiDVIHQirNSalyXI0irJyirnOYLCQub8Mro_7t377uuAoTc7FwpsGttidwgGpALJOKdqRO_-odvu4KdjDSRSMiE5EyMFR6rwXQgeK7P3oyo_GKBmMm8m82Yyb37Mj53bY8ch4h8vlVQ0UfwbyiF-ew</recordid><startdate>20141001</startdate><enddate>20141001</enddate><creator>Bashash, Saeid</creator><creator>Fathy, Hosam K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7TB</scope><scope>F28</scope></search><sort><creationdate>20141001</creationdate><title>Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals</title><author>Bashash, Saeid ; Fathy, Hosam K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-9a7aa7ab0ee7370a7b50848ce8002419814854a6883dd16da762dfb3be17ec6b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Batteries</topic><topic>Bidirectional</topic><topic>Charging</topic><topic>Electric batteries</topic><topic>Electric utilities</topic><topic>Electric vehicles</topic><topic>Electricity</topic><topic>Electricity pricing</topic><topic>Energy loss</topic><topic>Fuels</topic><topic>Hybrid vehicles</topic><topic>Integrated circuit modeling</topic><topic>Mathematical models</topic><topic>Optimization</topic><topic>power demand</topic><topic>Quadratic programming</topic><topic>smart grids</topic><topic>System-on-chip</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bashash, Saeid</creatorcontrib><creatorcontrib>Fathy, Hosam K.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bashash, Saeid</au><au>Fathy, Hosam K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2014-10-01</date><risdate>2014</risdate><volume>15</volume><issue>5</issue><spage>1958</spage><epage>1968</epage><pages>1958-1968</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>This paper develops a convex quadratic programming (QP) framework for the charge pattern optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally efficient PHEV charging model in the bidirectional vehicle-to-grid (V2G) integration studies, accounting for the hybrid powertrain dynamics and battery energy losses of the PHEVs. We adopt a previously developed PHEV power management system and construct a simplified model for the convex optimization problem. We use an equivalent circuit battery model to compute battery energy losses during grid charging and discharging. We then derive the total fuel and electricity cost of the PHEV as a quadratic function of battery state of charge and use a standard QP solver to minimize it for a few sample trips obtained from the National Household Travel Survey data set. Using a quad-core computer, the daily PHEV charging trajectory with 5-min time resolution can be optimized in less than tenth of a second. Through several examples, we show the application of the proposed method in various V2G-related problems, such as obtaining the aggregate load patterns of PHEVs, analyzing the potential impacts of large-scale bidirectional V2G integration, benchmarking the fuel economy of PHEVs, and determining the sensitivity of V2G load to abrupt price variations.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2014.2308283</doi><tpages>11</tpages></addata></record> |
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subjects | Batteries Bidirectional Charging Electric batteries Electric utilities Electric vehicles Electricity Electricity pricing Energy loss Fuels Hybrid vehicles Integrated circuit modeling Mathematical models Optimization power demand Quadratic programming smart grids System-on-chip |
title | Cost-Optimal Charging of Plug-In Hybrid Electric Vehicles Under Time-Varying Electricity Price Signals |
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