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Development of an online adaptive energy management strategy for the novel hierarchical coupled electric powertrain
This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) to exert its energy‐saving potential while considering the adaptability to driving conditions and the suppression of mode switching frequency. First, the complex...
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Published in: | Energy science & engineering 2021-09, Vol.9 (9), p.1596-1613 |
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description | This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) to exert its energy‐saving potential while considering the adaptability to driving conditions and the suppression of mode switching frequency. First, the complex energy management issue of the HCEP is simplified by introducing a simple power allocation method. And, the simplified energy management issue is solved by the Dynamic Programming to obtain the offline optimal working mode sequences of the HCEP. Second, the online working mode decision rules of the HCEP are established according to the obtained working mode sequences. And, the auxiliary rules in the decision rules are further optimized for different types of driving conditions. Then, the principal component analysis and generalized regression neural network are used to construct the driving condition recognizer (DCR) with high prediction accuracy. And, based on the constructed DCR, working mode decision rules, and introduced power allocation method, an online adaptive EMS is developed for the HCEP. Finally, the rationality of the introduced power allocation method and the effectiveness of the developed online adaptive EMS are verified.
This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) applied in vehicles. This online adaptive EMS can not only ensure the energy‐saving effect of the HCEP, but also can effectively avoid frequent working mode switching, as well as has adaptive ability to different driving conditions. |
doi_str_mv | 10.1002/ese3.931 |
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This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) applied in vehicles. This online adaptive EMS can not only ensure the energy‐saving effect of the HCEP, but also can effectively avoid frequent working mode switching, as well as has adaptive ability to different driving conditions.</description><identifier>ISSN: 2050-0505</identifier><identifier>EISSN: 2050-0505</identifier><identifier>DOI: 10.1002/ese3.931</identifier><language>eng</language><publisher>London: John Wiley & Sons, Inc</publisher><subject>Adaptability ; Consumption ; Convex analysis ; Driving conditions ; Dynamic programming ; Electric vehicles ; Energy conservation ; Energy efficiency ; Energy management ; energy saving ; hierarchical coupled electric powertrain ; Neural networks ; online adaptive energy management strategy ; Optimization algorithms ; power distribution ; Powertrain ; Principal components analysis ; Traffic ; Wheels ; working mode decision</subject><ispartof>Energy science & engineering, 2021-09, Vol.9 (9), p.1596-1613</ispartof><rights>2021 The Authors. published by the Society of Chemical Industry and John Wiley & Sons Ltd.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3931-56dced148e21db4305c1f44a7d1bce9810a86de88addb0ca2ee571447cfebc823</cites><orcidid>0000-0001-8157-2892</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2568165943/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2568165943?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,11562,25753,27924,27925,37012,44590,46052,46476,75126</link.rule.ids></links><search><creatorcontrib>Chen, Xianbao</creatorcontrib><creatorcontrib>Shu, Hongyu</creatorcontrib><creatorcontrib>Song, Yitong</creatorcontrib><title>Development of an online adaptive energy management strategy for the novel hierarchical coupled electric powertrain</title><title>Energy science & engineering</title><description>This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) to exert its energy‐saving potential while considering the adaptability to driving conditions and the suppression of mode switching frequency. First, the complex energy management issue of the HCEP is simplified by introducing a simple power allocation method. And, the simplified energy management issue is solved by the Dynamic Programming to obtain the offline optimal working mode sequences of the HCEP. Second, the online working mode decision rules of the HCEP are established according to the obtained working mode sequences. And, the auxiliary rules in the decision rules are further optimized for different types of driving conditions. Then, the principal component analysis and generalized regression neural network are used to construct the driving condition recognizer (DCR) with high prediction accuracy. And, based on the constructed DCR, working mode decision rules, and introduced power allocation method, an online adaptive EMS is developed for the HCEP. Finally, the rationality of the introduced power allocation method and the effectiveness of the developed online adaptive EMS are verified.
This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) applied in vehicles. This online adaptive EMS can not only ensure the energy‐saving effect of the HCEP, but also can effectively avoid frequent working mode switching, as well as has adaptive ability to different driving conditions.</description><subject>Adaptability</subject><subject>Consumption</subject><subject>Convex analysis</subject><subject>Driving conditions</subject><subject>Dynamic programming</subject><subject>Electric vehicles</subject><subject>Energy conservation</subject><subject>Energy efficiency</subject><subject>Energy management</subject><subject>energy saving</subject><subject>hierarchical coupled electric powertrain</subject><subject>Neural networks</subject><subject>online adaptive energy management strategy</subject><subject>Optimization algorithms</subject><subject>power distribution</subject><subject>Powertrain</subject><subject>Principal components analysis</subject><subject>Traffic</subject><subject>Wheels</subject><subject>working mode decision</subject><issn>2050-0505</issn><issn>2050-0505</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1kU1r3DAQhk1poCEJ5CcIeunFiWRLXvlY0m0bCOSQ5CzGo9GuFq_kSt6E_ffRZkPIpYdhhuGZd76q6lLwK8F5c02Z2qu-FV-q04YrXhdTXz_F36qLnDeccyGF7Lk4rfIveqYxTlsKM4uOQWAxjD4QAwvT7J-JUaC02rMtBFjRG5fnBDOVnIuJzWtiIRYRtvaUIOHaI4wM424ayTIaCefkkU3xhVIp9OG8OnEwZrp492fV0-_l483f-u7-z-3Nz7sa27JDrTqLZIXU1Ag7yJYrFE5KWFgxIPVacNCdJa3B2oEjNERqIaRcoKMBddOeVbdHXRthY6bkt5D2JoI3b4mYVgbS7HEkQ71adIQSdD9I7vQgLCJwoZzrem2haH0_ak0p_ttRns0m7lIo45tGdVp0qpdtoX4cKUwx50Tuo6vg5vAhc_iQKdsVtD6iL36k_X85s3xYHq7RvgICfJRq</recordid><startdate>202109</startdate><enddate>202109</enddate><creator>Chen, Xianbao</creator><creator>Shu, Hongyu</creator><creator>Song, Yitong</creator><general>John Wiley & Sons, Inc</general><general>Wiley</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8157-2892</orcidid></search><sort><creationdate>202109</creationdate><title>Development of an online adaptive energy management strategy for the novel hierarchical coupled electric powertrain</title><author>Chen, Xianbao ; Shu, Hongyu ; Song, Yitong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3931-56dced148e21db4305c1f44a7d1bce9810a86de88addb0ca2ee571447cfebc823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptability</topic><topic>Consumption</topic><topic>Convex analysis</topic><topic>Driving conditions</topic><topic>Dynamic programming</topic><topic>Electric vehicles</topic><topic>Energy conservation</topic><topic>Energy efficiency</topic><topic>Energy management</topic><topic>energy saving</topic><topic>hierarchical coupled electric powertrain</topic><topic>Neural networks</topic><topic>online adaptive energy management strategy</topic><topic>Optimization algorithms</topic><topic>power distribution</topic><topic>Powertrain</topic><topic>Principal components analysis</topic><topic>Traffic</topic><topic>Wheels</topic><topic>working mode decision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Xianbao</creatorcontrib><creatorcontrib>Shu, Hongyu</creatorcontrib><creatorcontrib>Song, Yitong</creatorcontrib><collection>Wiley Open Access</collection><collection>Wiley Online Library Journals</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)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest - 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><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Energy science & engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Xianbao</au><au>Shu, Hongyu</au><au>Song, Yitong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of an online adaptive energy management strategy for the novel hierarchical coupled electric powertrain</atitle><jtitle>Energy science & engineering</jtitle><date>2021-09</date><risdate>2021</risdate><volume>9</volume><issue>9</issue><spage>1596</spage><epage>1613</epage><pages>1596-1613</pages><issn>2050-0505</issn><eissn>2050-0505</eissn><abstract>This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) to exert its energy‐saving potential while considering the adaptability to driving conditions and the suppression of mode switching frequency. First, the complex energy management issue of the HCEP is simplified by introducing a simple power allocation method. And, the simplified energy management issue is solved by the Dynamic Programming to obtain the offline optimal working mode sequences of the HCEP. Second, the online working mode decision rules of the HCEP are established according to the obtained working mode sequences. And, the auxiliary rules in the decision rules are further optimized for different types of driving conditions. Then, the principal component analysis and generalized regression neural network are used to construct the driving condition recognizer (DCR) with high prediction accuracy. And, based on the constructed DCR, working mode decision rules, and introduced power allocation method, an online adaptive EMS is developed for the HCEP. Finally, the rationality of the introduced power allocation method and the effectiveness of the developed online adaptive EMS are verified.
This paper develops an online adaptive energy management strategy (EMS) for the promising hierarchical coupled electric powertrain (HCEP) applied in vehicles. This online adaptive EMS can not only ensure the energy‐saving effect of the HCEP, but also can effectively avoid frequent working mode switching, as well as has adaptive ability to different driving conditions.</abstract><cop>London</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/ese3.931</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-8157-2892</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptability Consumption Convex analysis Driving conditions Dynamic programming Electric vehicles Energy conservation Energy efficiency Energy management energy saving hierarchical coupled electric powertrain Neural networks online adaptive energy management strategy Optimization algorithms power distribution Powertrain Principal components analysis Traffic Wheels working mode decision |
title | Development of an online adaptive energy management strategy for the novel hierarchical coupled electric powertrain |
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