Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle
In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determinati...
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Veröffentlicht in: | Energy (Oxford) 2024-10, Vol.306, p.132368, Article 132368 |
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creator | Liu, Hanwu Lei, Yulong Sun, Wencai Chang, Cheng Jiang, Wei Liu, Yuwei Hu, Jianlong |
description | In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.
•An approximate optimal strategy is designed to realize the optimal power distribution.•Concept of OPDD in the intelligent decision is proposed to solve the MOO problem.•A fuzzy logic-based parameter adjustment model is designed based on V2X information. |
doi_str_mv | 10.1016/j.energy.2024.132368 |
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•An approximate optimal strategy is designed to realize the optimal power distribution.•Concept of OPDD in the intelligent decision is proposed to solve the MOO problem.•A fuzzy logic-based parameter adjustment model is designed based on V2X information.</description><identifier>ISSN: 0360-5442</identifier><identifier>DOI: 10.1016/j.energy.2024.132368</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>algorithms ; Approximate optimal control ; automation ; batteries ; Connected automated range-extended electric vehicle ; control methods ; electric vehicles ; energy ; Energy management strategy ; fuzzy logic ; Multi-objective optimization ; Multiple source information ; prediction</subject><ispartof>Energy (Oxford), 2024-10, Vol.306, p.132368, Article 132368</ispartof><rights>2024 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c288t-f889c6515d1bed7ab93385153203ba78aeb7a52ab95e6f5d4279662c5ff07d0b3</cites><orcidid>0009-0007-6281-9918</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S036054422402142X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Liu, Hanwu</creatorcontrib><creatorcontrib>Lei, Yulong</creatorcontrib><creatorcontrib>Sun, Wencai</creatorcontrib><creatorcontrib>Chang, Cheng</creatorcontrib><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>Liu, Yuwei</creatorcontrib><creatorcontrib>Hu, Jianlong</creatorcontrib><title>Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle</title><title>Energy (Oxford)</title><description>In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.
•An approximate optimal strategy is designed to realize the optimal power distribution.•Concept of OPDD in the intelligent decision is proposed to solve the MOO problem.•A fuzzy logic-based parameter adjustment model is designed based on V2X information.</description><subject>algorithms</subject><subject>Approximate optimal control</subject><subject>automation</subject><subject>batteries</subject><subject>Connected automated range-extended electric vehicle</subject><subject>control methods</subject><subject>electric vehicles</subject><subject>energy</subject><subject>Energy management strategy</subject><subject>fuzzy logic</subject><subject>Multi-objective optimization</subject><subject>Multiple source information</subject><subject>prediction</subject><issn>0360-5442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kLtuwyAUhj20UtPLG3Rg7OIUgy94qVRFvUmRKlXtjDAcJ1g2uICjpG_Qty6RM3eCA__5pP9LktsMLzOclffdEgy4zWFJMMmXGSW0ZGfJAtMSp0Wek4vk0vsOY1ywul4kvx_gQTi5RdYgMY7O7vUgAiA7hnjp0UxDgzBiAwOYgIRRaJj6oFPbdCCD3p3S-kcEHTG2RdIaE79AITEFewQq5ITZQAr7AEbFEfoYcFqiHWy17OE6OW9F7-HmdF4lX89Pn6vXdP3-8rZ6XKeSMBbSlrFalkVWqKwBVYmmppTFkRJMG1ExAU0lChLfCyjbQuWkqsuSyKJtcaVwQ6-Su5kbu35P4AMftJfQ98KAnTynkVVWjNEsRvM5Kp313kHLRxeluAPPMD_a5h2f_fCjbT7bjmsP8xrEGjsNjnupwUhQ2sXOXFn9P-APzLKQlQ</recordid><startdate>20241015</startdate><enddate>20241015</enddate><creator>Liu, Hanwu</creator><creator>Lei, Yulong</creator><creator>Sun, Wencai</creator><creator>Chang, Cheng</creator><creator>Jiang, Wei</creator><creator>Liu, Yuwei</creator><creator>Hu, Jianlong</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0009-0007-6281-9918</orcidid></search><sort><creationdate>20241015</creationdate><title>Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle</title><author>Liu, Hanwu ; Lei, Yulong ; Sun, Wencai ; Chang, Cheng ; Jiang, Wei ; Liu, Yuwei ; Hu, Jianlong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c288t-f889c6515d1bed7ab93385153203ba78aeb7a52ab95e6f5d4279662c5ff07d0b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>algorithms</topic><topic>Approximate optimal control</topic><topic>automation</topic><topic>batteries</topic><topic>Connected automated range-extended electric vehicle</topic><topic>control methods</topic><topic>electric vehicles</topic><topic>energy</topic><topic>Energy management strategy</topic><topic>fuzzy logic</topic><topic>Multi-objective optimization</topic><topic>Multiple source information</topic><topic>prediction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Hanwu</creatorcontrib><creatorcontrib>Lei, Yulong</creatorcontrib><creatorcontrib>Sun, Wencai</creatorcontrib><creatorcontrib>Chang, Cheng</creatorcontrib><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>Liu, Yuwei</creatorcontrib><creatorcontrib>Hu, Jianlong</creatorcontrib><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Hanwu</au><au>Lei, Yulong</au><au>Sun, Wencai</au><au>Chang, Cheng</au><au>Jiang, Wei</au><au>Liu, Yuwei</au><au>Hu, Jianlong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle</atitle><jtitle>Energy (Oxford)</jtitle><date>2024-10-15</date><risdate>2024</risdate><volume>306</volume><spage>132368</spage><pages>132368-</pages><artnum>132368</artnum><issn>0360-5442</issn><abstract>In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.
•An approximate optimal strategy is designed to realize the optimal power distribution.•Concept of OPDD in the intelligent decision is proposed to solve the MOO problem.•A fuzzy logic-based parameter adjustment model is designed based on V2X information.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2024.132368</doi><orcidid>https://orcid.org/0009-0007-6281-9918</orcidid></addata></record> |
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subjects | algorithms Approximate optimal control automation batteries Connected automated range-extended electric vehicle control methods electric vehicles energy Energy management strategy fuzzy logic Multi-objective optimization Multiple source information prediction |
title | Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle |
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