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Charge trajectory optimization of plug-in hybrid electric vehicles for energy cost reduction and battery health enhancement
This paper examines the problem of optimizing the charge trajectory of a plug-in hybrid electric vehicle (PHEV), defined as the timing and rate with which the PHEV obtains electricity from the power grid. Two objectives are considered in this optimization. First, we minimize the total cost of fuel a...
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creator | Bashash, S Moura, S J Fathy, H K |
description | This paper examines the problem of optimizing the charge trajectory of a plug-in hybrid electric vehicle (PHEV), defined as the timing and rate with which the PHEV obtains electricity from the power grid. Two objectives are considered in this optimization. First, we minimize the total cost of fuel and electricity consumed by the PHEV over a 24-hour naturalistic drive cycle. We predict this cost using a previously-developed stochastic optimal PHEV power management strategy. Second, we also minimize total battery health degradation over the course of the 24-hour cycle. This degradation is predicted using an electrochemistry-based model of anode-side resistive film formation in Li-ion batteries. The paper shows that these two objectives are conflicting, and trades them off using a non-dominated sort genetic algorithm, NSGA-II. As a result, a Pareto front of optimal PHEV charge trajectories is obtained. The effects of electricity price and trip schedule on the Pareto front are analyzed and discussed. |
doi_str_mv | 10.1109/ACC.2010.5530497 |
format | conference_proceeding |
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Two objectives are considered in this optimization. First, we minimize the total cost of fuel and electricity consumed by the PHEV over a 24-hour naturalistic drive cycle. We predict this cost using a previously-developed stochastic optimal PHEV power management strategy. Second, we also minimize total battery health degradation over the course of the 24-hour cycle. This degradation is predicted using an electrochemistry-based model of anode-side resistive film formation in Li-ion batteries. The paper shows that these two objectives are conflicting, and trades them off using a non-dominated sort genetic algorithm, NSGA-II. As a result, a Pareto front of optimal PHEV charge trajectories is obtained. 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The effects of electricity price and trip schedule on the Pareto front are analyzed and discussed.</description><subject>Batteries</subject><subject>Cost function</subject><subject>Degradation</subject><subject>Energy consumption</subject><subject>Energy management</subject><subject>Fuels</subject><subject>Hybrid electric vehicles</subject><subject>Power grids</subject><subject>Stochastic processes</subject><subject>Timing</subject><issn>0743-1619</issn><issn>2378-5861</issn><isbn>9781424474264</isbn><isbn>1424474264</isbn><isbn>1424474256</isbn><isbn>1424474272</isbn><isbn>9781424474271</isbn><isbn>9781424474257</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1UMlqwzAUVDdokuZe6EU_4FSStR6D6QaBXtpzkKWnWMGxg6wU3P58TZfTMMwCMwjdUrKilJj7dVWtGJmYECXhRp2hOeWMc8WZkOdoxkqlC6ElvUBLo_S_JvklmhHFy4JKaq7RfBj2hFBjJJmhr6qxaQc4J7sHl_s04v6Y4yF-2hz7DvcBH9vTrogdbsY6RY-hnXwpOvwBTXQtDDj0CUMHaTdi1w8ZJ_An95O2nce1zRmm2gZsm5vJ2NjOwQG6fIOugm0HWP7hAr0_PrxVz8Xm9emlWm-KSJXIhROMMu_BT1NqpxhRIhBbcwgMuACtNPGaEa0UVcooKSSpaW1MENL4wEK5QHe_vREAtscUDzaN278Ty28NwWOX</recordid><startdate>201006</startdate><enddate>201006</enddate><creator>Bashash, S</creator><creator>Moura, S J</creator><creator>Fathy, H K</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201006</creationdate><title>Charge trajectory optimization of plug-in hybrid electric vehicles for energy cost reduction and battery health enhancement</title><author>Bashash, S ; Moura, S J ; Fathy, H K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c5212dded426bc72075f0ab4ef2e45e8780d820877177976560b1b99f569df2f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Batteries</topic><topic>Cost function</topic><topic>Degradation</topic><topic>Energy consumption</topic><topic>Energy management</topic><topic>Fuels</topic><topic>Hybrid electric vehicles</topic><topic>Power grids</topic><topic>Stochastic processes</topic><topic>Timing</topic><toplevel>online_resources</toplevel><creatorcontrib>Bashash, S</creatorcontrib><creatorcontrib>Moura, S J</creatorcontrib><creatorcontrib>Fathy, H K</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bashash, S</au><au>Moura, S J</au><au>Fathy, H K</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Charge trajectory optimization of plug-in hybrid electric vehicles for energy cost reduction and battery health enhancement</atitle><btitle>Proceedings of the 2010 American Control Conference</btitle><stitle>ACC</stitle><date>2010-06</date><risdate>2010</risdate><spage>5824</spage><epage>5831</epage><pages>5824-5831</pages><issn>0743-1619</issn><eissn>2378-5861</eissn><isbn>9781424474264</isbn><isbn>1424474264</isbn><eisbn>1424474256</eisbn><eisbn>1424474272</eisbn><eisbn>9781424474271</eisbn><eisbn>9781424474257</eisbn><abstract>This paper examines the problem of optimizing the charge trajectory of a plug-in hybrid electric vehicle (PHEV), defined as the timing and rate with which the PHEV obtains electricity from the power grid. Two objectives are considered in this optimization. First, we minimize the total cost of fuel and electricity consumed by the PHEV over a 24-hour naturalistic drive cycle. We predict this cost using a previously-developed stochastic optimal PHEV power management strategy. Second, we also minimize total battery health degradation over the course of the 24-hour cycle. This degradation is predicted using an electrochemistry-based model of anode-side resistive film formation in Li-ion batteries. The paper shows that these two objectives are conflicting, and trades them off using a non-dominated sort genetic algorithm, NSGA-II. As a result, a Pareto front of optimal PHEV charge trajectories is obtained. The effects of electricity price and trip schedule on the Pareto front are analyzed and discussed.</abstract><pub>IEEE</pub><doi>10.1109/ACC.2010.5530497</doi><tpages>8</tpages></addata></record> |
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ispartof | Proceedings of the 2010 American Control Conference, 2010, p.5824-5831 |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Batteries Cost function Degradation Energy consumption Energy management Fuels Hybrid electric vehicles Power grids Stochastic processes Timing |
title | Charge trajectory optimization of plug-in hybrid electric vehicles for energy cost reduction and battery health enhancement |
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