Online Optimal Energy Distribution of Composite Power Vehicles Based on BP Neural Network Velocity Prediction
In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and e...
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| Veröffentlicht in: | Mathematical problems in engineering 2021-09, Vol.2021, p.1-10 |
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| container_title | Mathematical problems in engineering |
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| creator | Jiang, Qingjian Fu, Zhijun Hu, Qiang |
| description | In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and elapsed vehicle speed information is used as the input. Then, according to the predicted vehicle speed, an energy management method based on model predictive control is proposed, and online real-time power distribution is carried out through rolling optimization and feedback correction. Cosimulation results under urban drive cycle show that the proposed method can effectively improve the energy efficiency of composite power sources compared with the commonly used method with the assumption of prior known driving conditions. |
| doi_str_mv | 10.1155/2021/2519569 |
| format | Article |
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Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and elapsed vehicle speed information is used as the input. Then, according to the predicted vehicle speed, an energy management method based on model predictive control is proposed, and online real-time power distribution is carried out through rolling optimization and feedback correction. Cosimulation results under urban drive cycle show that the proposed method can effectively improve the energy efficiency of composite power sources compared with the commonly used method with the assumption of prior known driving conditions.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/2519569</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Advisors ; Algorithms ; Back propagation ; Back propagation networks ; Control theory ; Driving conditions ; Electric power distribution ; Electric vehicles ; Energy distribution ; Energy efficiency ; Energy management ; Lithium ; Neural networks ; Optimization ; Power sources ; Power supply ; Predictive control ; Simulation ; Traffic speed ; Velocity</subject><ispartof>Mathematical problems in engineering, 2021-09, Vol.2021, p.1-10</ispartof><rights>Copyright © 2021 Qingjian Jiang et al.</rights><rights>Copyright © 2021 Qingjian Jiang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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Fu, Zhijun ; Hu, Qiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-4ca1feb8b8d654516c4699991923ece9c77a0f4118cc8ff9ab1abc311bf5e0c33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Advisors</topic><topic>Algorithms</topic><topic>Back propagation</topic><topic>Back propagation networks</topic><topic>Control theory</topic><topic>Driving conditions</topic><topic>Electric power distribution</topic><topic>Electric vehicles</topic><topic>Energy distribution</topic><topic>Energy efficiency</topic><topic>Energy management</topic><topic>Lithium</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Power sources</topic><topic>Power supply</topic><topic>Predictive control</topic><topic>Simulation</topic><topic>Traffic speed</topic><topic>Velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Qingjian</creatorcontrib><creatorcontrib>Fu, Zhijun</creatorcontrib><creatorcontrib>Hu, Qiang</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Qingjian</au><au>Fu, Zhijun</au><au>Hu, Qiang</au><au>Pekař, Libor</au><au>Libor Pekař</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Online Optimal Energy Distribution of Composite Power Vehicles Based on BP Neural Network Velocity Prediction</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2021-09-15</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. 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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | Advisors Algorithms Back propagation Back propagation networks Control theory Driving conditions Electric power distribution Electric vehicles Energy distribution Energy efficiency Energy management Lithium Neural networks Optimization Power sources Power supply Predictive control Simulation Traffic speed Velocity |
| title | Online Optimal Energy Distribution of Composite Power Vehicles Based on BP Neural Network Velocity Prediction |
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