Algorithms for Battery Utilization in Electric Vehicles
We consider the problem of utilizing a pack of m batteries serving n current demands in electric vehicles. When serving a demand, the current allocation might be split among the batteries in the pack. A battery's life depends on the discharge current used for supplying the requests. Any deviati...
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| Veröffentlicht in: | Applied artificial intelligence 2014-03, Vol.28 (3), p.272-291 |
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| container_title | Applied artificial intelligence |
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| creator | Adany, Ron Tamir, Tami |
| description | We consider the problem of utilizing a pack of m batteries serving n current demands in electric vehicles. When serving a demand, the current allocation might be split among the batteries in the pack. A battery's life depends on the discharge current used for supplying the requests. Any deviation from the optimal discharge-current is associated with a penalty. Thus, the problem is to serve an online sequence of current requests in a way that minimizes the total penalty associated with the service.
We show that the offline problem, for which the sequence of current demands is known in advance, is strongly NP-hard and hard to approximate within an additive gap of Ω(m) from the optimum. For the online problem, we present a competitive algorithm associated with the redundant penalty at most m. Finally, we provide a lower bound of 1.5 for the multiplicative competitive ratio of any online algorithm. |
| doi_str_mv | 10.1080/08839514.2014.883906 |
| format | Article |
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We show that the offline problem, for which the sequence of current demands is known in advance, is strongly NP-hard and hard to approximate within an additive gap of Ω(m) from the optimum. For the online problem, we present a competitive algorithm associated with the redundant penalty at most m. Finally, we provide a lower bound of 1.5 for the multiplicative competitive ratio of any online algorithm.</description><identifier>ISSN: 0883-9514</identifier><identifier>EISSN: 1087-6545</identifier><identifier>DOI: 10.1080/08839514.2014.883906</identifier><language>eng</language><publisher>Philadelphia: Taylor & Francis</publisher><subject>Algorithms ; Batteries ; Demand ; Electric batteries ; Electric discharges ; Electric vehicles ; On-line systems ; Online ; Optimization</subject><ispartof>Applied artificial intelligence, 2014-03, Vol.28 (3), p.272-291</ispartof><rights>Copyright © 2014 Taylor & Francis Group, LLC 2014</rights><rights>Copyright Taylor & Francis Ltd. 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c317t-100e5911edb22796d421c4d5f464311b30c430797767d740d7652ffb0f3d70203</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Adany, Ron</creatorcontrib><creatorcontrib>Tamir, Tami</creatorcontrib><title>Algorithms for Battery Utilization in Electric Vehicles</title><title>Applied artificial intelligence</title><description>We consider the problem of utilizing a pack of m batteries serving n current demands in electric vehicles. When serving a demand, the current allocation might be split among the batteries in the pack. A battery's life depends on the discharge current used for supplying the requests. Any deviation from the optimal discharge-current is associated with a penalty. Thus, the problem is to serve an online sequence of current requests in a way that minimizes the total penalty associated with the service.
We show that the offline problem, for which the sequence of current demands is known in advance, is strongly NP-hard and hard to approximate within an additive gap of Ω(m) from the optimum. For the online problem, we present a competitive algorithm associated with the redundant penalty at most m. Finally, we provide a lower bound of 1.5 for the multiplicative competitive ratio of any online algorithm.</description><subject>Algorithms</subject><subject>Batteries</subject><subject>Demand</subject><subject>Electric batteries</subject><subject>Electric discharges</subject><subject>Electric vehicles</subject><subject>On-line systems</subject><subject>Online</subject><subject>Optimization</subject><issn>0883-9514</issn><issn>1087-6545</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EEqXwDxgisbCknD9iJxMqqHxIlVgoq5U6DnXlxMV2hcqvx1FgYWC50-me93R6ELrEMMNQwg2UJa0KzGYEUhkG4EdoknYi5wUrjtFkQPKBOUVnIWwBAAuBJ0jM7bvzJm66kLXOZ3d1jNofslU01nzV0bg-M322sFpFb1T2pjdGWR3O0Ulb26AvfvoUrR4Wr_dP-fLl8fl-vswVxSLmGEAXFca6WRMiKt4wghVripZxRjFeU1CMgqiE4KIRDBrBC9K2a2hpI4AAnaLr8e7Ou4-9DlF2Jihtbd1rtw8SFxQqUbKSJPTqD7p1e9-n7xKFgRNeAU0UGynlXQhet3LnTVf7g8QgB5vy16YcbMrRZordjjHTJ01d_em8bWSsD9b51te9MkHSfy98A05TeGc</recordid><startdate>20140316</startdate><enddate>20140316</enddate><creator>Adany, Ron</creator><creator>Tamir, Tami</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7SP</scope><scope>7TB</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20140316</creationdate><title>Algorithms for Battery Utilization in Electric Vehicles</title><author>Adany, Ron ; Tamir, Tami</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-100e5911edb22796d421c4d5f464311b30c430797767d740d7652ffb0f3d70203</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Algorithms</topic><topic>Batteries</topic><topic>Demand</topic><topic>Electric batteries</topic><topic>Electric discharges</topic><topic>Electric vehicles</topic><topic>On-line systems</topic><topic>Online</topic><topic>Optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adany, Ron</creatorcontrib><creatorcontrib>Tamir, Tami</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</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>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>Applied artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adany, Ron</au><au>Tamir, Tami</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Algorithms for Battery Utilization in Electric Vehicles</atitle><jtitle>Applied artificial intelligence</jtitle><date>2014-03-16</date><risdate>2014</risdate><volume>28</volume><issue>3</issue><spage>272</spage><epage>291</epage><pages>272-291</pages><issn>0883-9514</issn><eissn>1087-6545</eissn><abstract>We consider the problem of utilizing a pack of m batteries serving n current demands in electric vehicles. When serving a demand, the current allocation might be split among the batteries in the pack. A battery's life depends on the discharge current used for supplying the requests. Any deviation from the optimal discharge-current is associated with a penalty. Thus, the problem is to serve an online sequence of current requests in a way that minimizes the total penalty associated with the service.
We show that the offline problem, for which the sequence of current demands is known in advance, is strongly NP-hard and hard to approximate within an additive gap of Ω(m) from the optimum. For the online problem, we present a competitive algorithm associated with the redundant penalty at most m. Finally, we provide a lower bound of 1.5 for the multiplicative competitive ratio of any online algorithm.</abstract><cop>Philadelphia</cop><pub>Taylor & Francis</pub><doi>10.1080/08839514.2014.883906</doi><tpages>20</tpages></addata></record> |
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| ispartof | Applied artificial intelligence, 2014-03, Vol.28 (3), p.272-291 |
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| language | eng |
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| source | EBSCOhost Business Source Complete |
| subjects | Algorithms Batteries Demand Electric batteries Electric discharges Electric vehicles On-line systems Online Optimization |
| title | Algorithms for Battery Utilization in Electric Vehicles |
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