Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles
In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in...
Gespeichert in:
| Veröffentlicht in: | Electronics (Basel) 2022-07, Vol.11 (13), p.1925 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 13 |
| container_start_page | 1925 |
| container_title | Electronics (Basel) |
| container_volume | 11 |
| creator | Oubelaid, Adel Taib, Nabil Nikolovski, Srete Alharbi, Turki E. A. Rekioua, Toufik Flah, Aymen Ghoneim, Sherif S. M. |
| description | In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods. |
| doi_str_mv | 10.3390/electronics11131925 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2685981509</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A723418535</galeid><sourcerecordid>A723418535</sourcerecordid><originalsourceid>FETCH-LOGICAL-c361t-7dc0744c92c03a6ee0b16661eb93273a6f4d9057f8a08eba710fd2c6063d1fda3</originalsourceid><addsrcrecordid>eNptUUtLAzEQXkTBov0FXgKet-axrxzLWrVQUPBxXdJksqakSU22hfrrTa2CB2cOM8w33zczTJZdETxhjOMbsCCH4J2RkRDCCKflSTaiuOY5p5ye_snPs3GMK5yME9YwPMo-524Aa00PbkDPGwCFWu-SnEXCKfQEQfuwFk4CmrsdxMH0YjDeIa-RQG9psg-_DJvIs-9ljEzQu5EWDlg0CoJxPboNZneI7T4h8TI708JGGP_Ei-z1bvbSPuSLx_t5O13kklVkyGslcV0UklOJmagA8JJUVUVgyRmtU0UXiuOy1o3ADSxFTbBWVFa4YopoJdhFdn3U3QT_sU0ndCu_DS6N7GjVlLwhJeapa3Ls6oWFzjjthyBkcgVrI70DbVJ9WlNWkKZkZSKwI0EGH2MA3W2CWYuw7wjuDo_p_nkM-wI6WoWY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2685981509</pqid></control><display><type>article</type><title>Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Oubelaid, Adel ; Taib, Nabil ; Nikolovski, Srete ; Alharbi, Turki E. A. ; Rekioua, Toufik ; Flah, Aymen ; Ghoneim, Sherif S. M.</creator><creatorcontrib>Oubelaid, Adel ; Taib, Nabil ; Nikolovski, Srete ; Alharbi, Turki E. A. ; Rekioua, Toufik ; Flah, Aymen ; Ghoneim, Sherif S. M.</creatorcontrib><description>In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics11131925</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Alternative energy sources ; Automotive engineering ; Battery cycles ; Biogeography ; Closed loops ; Consumption ; Control systems ; Controllers ; Convex analysis ; Cost function ; Design and construction ; Electric vehicles ; Emissions ; Energy management ; Energy resources ; Environmental testing ; Feedback control ; Genetic algorithms ; Mathematical optimization ; Methods ; Optimization algorithms ; Optimization techniques ; Parameters ; Particle swarm optimization ; Permanent magnets ; Regulation ; Renewable resources ; Speed control ; Synchronous motors ; Tires ; Trends ; Tuning</subject><ispartof>Electronics (Basel), 2022-07, Vol.11 (13), p.1925</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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><citedby>FETCH-LOGICAL-c361t-7dc0744c92c03a6ee0b16661eb93273a6f4d9057f8a08eba710fd2c6063d1fda3</citedby><cites>FETCH-LOGICAL-c361t-7dc0744c92c03a6ee0b16661eb93273a6f4d9057f8a08eba710fd2c6063d1fda3</cites><orcidid>0000-0002-9033-4332 ; 0000-0001-5770-5923 ; 0000-0003-2659-5631 ; 0000-0002-3463-6096 ; 0000-0002-5499-6953 ; 0000-0002-9387-1950</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Oubelaid, Adel</creatorcontrib><creatorcontrib>Taib, Nabil</creatorcontrib><creatorcontrib>Nikolovski, Srete</creatorcontrib><creatorcontrib>Alharbi, Turki E. A.</creatorcontrib><creatorcontrib>Rekioua, Toufik</creatorcontrib><creatorcontrib>Flah, Aymen</creatorcontrib><creatorcontrib>Ghoneim, Sherif S. M.</creatorcontrib><title>Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles</title><title>Electronics (Basel)</title><description>In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.</description><subject>Algorithms</subject><subject>Alternative energy sources</subject><subject>Automotive engineering</subject><subject>Battery cycles</subject><subject>Biogeography</subject><subject>Closed loops</subject><subject>Consumption</subject><subject>Control systems</subject><subject>Controllers</subject><subject>Convex analysis</subject><subject>Cost function</subject><subject>Design and construction</subject><subject>Electric vehicles</subject><subject>Emissions</subject><subject>Energy management</subject><subject>Energy resources</subject><subject>Environmental testing</subject><subject>Feedback control</subject><subject>Genetic algorithms</subject><subject>Mathematical optimization</subject><subject>Methods</subject><subject>Optimization algorithms</subject><subject>Optimization techniques</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Permanent magnets</subject><subject>Regulation</subject><subject>Renewable resources</subject><subject>Speed control</subject><subject>Synchronous motors</subject><subject>Tires</subject><subject>Trends</subject><subject>Tuning</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUUtLAzEQXkTBov0FXgKet-axrxzLWrVQUPBxXdJksqakSU22hfrrTa2CB2cOM8w33zczTJZdETxhjOMbsCCH4J2RkRDCCKflSTaiuOY5p5ye_snPs3GMK5yME9YwPMo-524Aa00PbkDPGwCFWu-SnEXCKfQEQfuwFk4CmrsdxMH0YjDeIa-RQG9psg-_DJvIs-9ljEzQu5EWDlg0CoJxPboNZneI7T4h8TI708JGGP_Ei-z1bvbSPuSLx_t5O13kklVkyGslcV0UklOJmagA8JJUVUVgyRmtU0UXiuOy1o3ADSxFTbBWVFa4YopoJdhFdn3U3QT_sU0ndCu_DS6N7GjVlLwhJeapa3Ls6oWFzjjthyBkcgVrI70DbVJ9WlNWkKZkZSKwI0EGH2MA3W2CWYuw7wjuDo_p_nkM-wI6WoWY</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Oubelaid, Adel</creator><creator>Taib, Nabil</creator><creator>Nikolovski, Srete</creator><creator>Alharbi, Turki E. A.</creator><creator>Rekioua, Toufik</creator><creator>Flah, Aymen</creator><creator>Ghoneim, Sherif S. M.</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-9033-4332</orcidid><orcidid>https://orcid.org/0000-0001-5770-5923</orcidid><orcidid>https://orcid.org/0000-0003-2659-5631</orcidid><orcidid>https://orcid.org/0000-0002-3463-6096</orcidid><orcidid>https://orcid.org/0000-0002-5499-6953</orcidid><orcidid>https://orcid.org/0000-0002-9387-1950</orcidid></search><sort><creationdate>20220701</creationdate><title>Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles</title><author>Oubelaid, Adel ; Taib, Nabil ; Nikolovski, Srete ; Alharbi, Turki E. A. ; Rekioua, Toufik ; Flah, Aymen ; Ghoneim, Sherif S. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-7dc0744c92c03a6ee0b16661eb93273a6f4d9057f8a08eba710fd2c6063d1fda3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Alternative energy sources</topic><topic>Automotive engineering</topic><topic>Battery cycles</topic><topic>Biogeography</topic><topic>Closed loops</topic><topic>Consumption</topic><topic>Control systems</topic><topic>Controllers</topic><topic>Convex analysis</topic><topic>Cost function</topic><topic>Design and construction</topic><topic>Electric vehicles</topic><topic>Emissions</topic><topic>Energy management</topic><topic>Energy resources</topic><topic>Environmental testing</topic><topic>Feedback control</topic><topic>Genetic algorithms</topic><topic>Mathematical optimization</topic><topic>Methods</topic><topic>Optimization algorithms</topic><topic>Optimization techniques</topic><topic>Parameters</topic><topic>Particle swarm optimization</topic><topic>Permanent magnets</topic><topic>Regulation</topic><topic>Renewable resources</topic><topic>Speed control</topic><topic>Synchronous motors</topic><topic>Tires</topic><topic>Trends</topic><topic>Tuning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Oubelaid, Adel</creatorcontrib><creatorcontrib>Taib, Nabil</creatorcontrib><creatorcontrib>Nikolovski, Srete</creatorcontrib><creatorcontrib>Alharbi, Turki E. A.</creatorcontrib><creatorcontrib>Rekioua, Toufik</creatorcontrib><creatorcontrib>Flah, Aymen</creatorcontrib><creatorcontrib>Ghoneim, Sherif S. M.</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</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><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Oubelaid, Adel</au><au>Taib, Nabil</au><au>Nikolovski, Srete</au><au>Alharbi, Turki E. A.</au><au>Rekioua, Toufik</au><au>Flah, Aymen</au><au>Ghoneim, Sherif S. M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles</atitle><jtitle>Electronics (Basel)</jtitle><date>2022-07-01</date><risdate>2022</risdate><volume>11</volume><issue>13</issue><spage>1925</spage><pages>1925-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics11131925</doi><orcidid>https://orcid.org/0000-0002-9033-4332</orcidid><orcidid>https://orcid.org/0000-0001-5770-5923</orcidid><orcidid>https://orcid.org/0000-0003-2659-5631</orcidid><orcidid>https://orcid.org/0000-0002-3463-6096</orcidid><orcidid>https://orcid.org/0000-0002-5499-6953</orcidid><orcidid>https://orcid.org/0000-0002-9387-1950</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2079-9292 |
| ispartof | Electronics (Basel), 2022-07, Vol.11 (13), p.1925 |
| issn | 2079-9292 2079-9292 |
| language | eng |
| recordid | cdi_proquest_journals_2685981509 |
| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Alternative energy sources Automotive engineering Battery cycles Biogeography Closed loops Consumption Control systems Controllers Convex analysis Cost function Design and construction Electric vehicles Emissions Energy management Energy resources Environmental testing Feedback control Genetic algorithms Mathematical optimization Methods Optimization algorithms Optimization techniques Parameters Particle swarm optimization Permanent magnets Regulation Renewable resources Speed control Synchronous motors Tires Trends Tuning |
| title | Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T22%3A48%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Intelligent%20Speed%20Control%20and%20Performance%20Investigation%20of%20a%20Vector%20Controlled%20Electric%20Vehicle%20Considering%20Driving%20Cycles&rft.jtitle=Electronics%20(Basel)&rft.au=Oubelaid,%20Adel&rft.date=2022-07-01&rft.volume=11&rft.issue=13&rft.spage=1925&rft.pages=1925-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics11131925&rft_dat=%3Cgale_proqu%3EA723418535%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2685981509&rft_id=info:pmid/&rft_galeid=A723418535&rfr_iscdi=true |