A predictive power management scheme for hybrid energy storage system in electric vehicle
This paper presents a model predictive control (MPC) approach for energy management of a hybrid energy storage system (HESS), in an electric vehicle (EV). HESS constitutes the battery and the supercapacitor (SC) where the latter is used as an auxiliary source to reduce stress on the battery. Hence,...
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Veröffentlicht in: | International journal of circuit theory and applications 2021-11, Vol.49 (11), p.3864-3878 |
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description | This paper presents a model predictive control (MPC) approach for energy management of a hybrid energy storage system (HESS), in an electric vehicle (EV). HESS constitutes the battery and the supercapacitor (SC) where the latter is used as an auxiliary source to reduce stress on the battery. Hence, an appropriate control strategy should be formulated for allocating low‐frequency power fluctuations to the battery and high‐frequency power fluctuations to SC. The conventional PI‐based control strategy has a difficult tuning process, and its performance is affected when the operating point fluctuates. Therefore, an MPC‐based control strategy is proposed because of its simplicity, intuitiveness, ease of implementation, and inclusion of nonlinearities and constraints. This paper provides a complete power management strategy for the HESS system, using a modified MPC approach which is simpler compared to the traditional MPC, for a multiple‐input bidirectional DC/DC converter. Firstly, the model of a dual input bidirectional DC/DC converter is developed, and a two‐loop control strategy is developed with predictive inner current control and outer voltage control for DC‐link voltage regulation and HESS energy management. Secondly, an SC voltage regulation loop is developed for enabling the charging and discharging of SC for reliable operation of the HESS system. Finally, simulation studies are done using MATLAB Simulink, and a prototype experimental setup is developed to validate the effectiveness of the proposed control strategy through the dSPACE platform.
A model predictive control‐based control strategy is developed for a multiple‐input bidirectional DC/DC converter with a hybrid energy storage system of an electric vehicle. The proposed control strategy is compared with the conventional PID‐based strategy to validate the effectiveness of the proposed strategy in terms of faster DC‐link voltage regulation and complete control of battery discharge rate. The proposed strategy reduces stress on the battery and ensures the reliable operation of the converter with supercapacitor voltage regulation. |
doi_str_mv | 10.1002/cta.3119 |
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A model predictive control‐based control strategy is developed for a multiple‐input bidirectional DC/DC converter with a hybrid energy storage system of an electric vehicle. The proposed control strategy is compared with the conventional PID‐based strategy to validate the effectiveness of the proposed strategy in terms of faster DC‐link voltage regulation and complete control of battery discharge rate. The proposed strategy reduces stress on the battery and ensures the reliable operation of the converter with supercapacitor voltage regulation.</description><identifier>ISSN: 0098-9886</identifier><identifier>EISSN: 1097-007X</identifier><identifier>DOI: 10.1002/cta.3119</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>Batteries ; bidirectional DC/DC converter ; Electric converters ; Electric potential ; electric vehicle (EV) ; Electric vehicles ; Energy management ; Energy storage ; hybrid energy storage system (HESS) ; Hybrid systems ; model predictive control (MPC) ; Power management ; Predictive control ; Voltage</subject><ispartof>International journal of circuit theory and applications, 2021-11, Vol.49 (11), p.3864-3878</ispartof><rights>2021 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2939-e452641b583ac6f9b417949eac2340322818ad4abce6c648e8f2abcbfb2db0883</citedby><cites>FETCH-LOGICAL-c2939-e452641b583ac6f9b417949eac2340322818ad4abce6c648e8f2abcbfb2db0883</cites><orcidid>0000-0001-9426-5189</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcta.3119$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcta.3119$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids></links><search><creatorcontrib>Katnapally, Akhil</creatorcontrib><creatorcontrib>Manthati, Udaya Bhasker</creatorcontrib><creatorcontrib>Chirayarukil Raveendran, Arunkumar</creatorcontrib><creatorcontrib>Punna, Srinivas</creatorcontrib><title>A predictive power management scheme for hybrid energy storage system in electric vehicle</title><title>International journal of circuit theory and applications</title><description>This paper presents a model predictive control (MPC) approach for energy management of a hybrid energy storage system (HESS), in an electric vehicle (EV). HESS constitutes the battery and the supercapacitor (SC) where the latter is used as an auxiliary source to reduce stress on the battery. Hence, an appropriate control strategy should be formulated for allocating low‐frequency power fluctuations to the battery and high‐frequency power fluctuations to SC. The conventional PI‐based control strategy has a difficult tuning process, and its performance is affected when the operating point fluctuates. Therefore, an MPC‐based control strategy is proposed because of its simplicity, intuitiveness, ease of implementation, and inclusion of nonlinearities and constraints. This paper provides a complete power management strategy for the HESS system, using a modified MPC approach which is simpler compared to the traditional MPC, for a multiple‐input bidirectional DC/DC converter. Firstly, the model of a dual input bidirectional DC/DC converter is developed, and a two‐loop control strategy is developed with predictive inner current control and outer voltage control for DC‐link voltage regulation and HESS energy management. Secondly, an SC voltage regulation loop is developed for enabling the charging and discharging of SC for reliable operation of the HESS system. Finally, simulation studies are done using MATLAB Simulink, and a prototype experimental setup is developed to validate the effectiveness of the proposed control strategy through the dSPACE platform.
A model predictive control‐based control strategy is developed for a multiple‐input bidirectional DC/DC converter with a hybrid energy storage system of an electric vehicle. The proposed control strategy is compared with the conventional PID‐based strategy to validate the effectiveness of the proposed strategy in terms of faster DC‐link voltage regulation and complete control of battery discharge rate. The proposed strategy reduces stress on the battery and ensures the reliable operation of the converter with supercapacitor voltage regulation.</description><subject>Batteries</subject><subject>bidirectional DC/DC converter</subject><subject>Electric converters</subject><subject>Electric potential</subject><subject>electric vehicle (EV)</subject><subject>Electric vehicles</subject><subject>Energy management</subject><subject>Energy storage</subject><subject>hybrid energy storage system (HESS)</subject><subject>Hybrid systems</subject><subject>model predictive control (MPC)</subject><subject>Power management</subject><subject>Predictive control</subject><subject>Voltage</subject><issn>0098-9886</issn><issn>1097-007X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp10E1LAzEQBuAgCtYq-BMCXrxszdfuJsdStAoFLxX0FLLZ2TZlv0zSlv33bq1XTzMDD_PCi9A9JTNKCHuy0cw4peoCTShReUJI_nmJJoQomSgps2t0E8KOECIZVxP0Nce9h9LZ6A6A--4IHjemNRtooI042O244KrzeDsU3pUYWvCbAYfY-RHhMIQIDXYthhps9M7iA2ydreEWXVWmDnD3N6fo4-V5vXhNVu_Lt8V8lVimuEpApCwTtEglNzarVCForoQCYxkXhDMmqTSlMIWFzGZCgqzYeBRVwcqCSMmn6OH8t_fd9x5C1Ltu79sxUrNU5TwVdAyaosezsr4LwUOle-8a4wdNiT4Vp8fi9Km4kSZnenQ1DP86vVjPf_0P8LZvnw</recordid><startdate>202111</startdate><enddate>202111</enddate><creator>Katnapally, Akhil</creator><creator>Manthati, Udaya Bhasker</creator><creator>Chirayarukil Raveendran, Arunkumar</creator><creator>Punna, Srinivas</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-9426-5189</orcidid></search><sort><creationdate>202111</creationdate><title>A predictive power management scheme for hybrid energy storage system in electric vehicle</title><author>Katnapally, Akhil ; Manthati, Udaya Bhasker ; Chirayarukil Raveendran, Arunkumar ; Punna, Srinivas</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2939-e452641b583ac6f9b417949eac2340322818ad4abce6c648e8f2abcbfb2db0883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Batteries</topic><topic>bidirectional DC/DC converter</topic><topic>Electric converters</topic><topic>Electric potential</topic><topic>electric vehicle (EV)</topic><topic>Electric vehicles</topic><topic>Energy management</topic><topic>Energy storage</topic><topic>hybrid energy storage system (HESS)</topic><topic>Hybrid systems</topic><topic>model predictive control (MPC)</topic><topic>Power management</topic><topic>Predictive control</topic><topic>Voltage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Katnapally, Akhil</creatorcontrib><creatorcontrib>Manthati, Udaya Bhasker</creatorcontrib><creatorcontrib>Chirayarukil Raveendran, Arunkumar</creatorcontrib><creatorcontrib>Punna, Srinivas</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of circuit theory and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Katnapally, Akhil</au><au>Manthati, Udaya Bhasker</au><au>Chirayarukil Raveendran, Arunkumar</au><au>Punna, Srinivas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A predictive power management scheme for hybrid energy storage system in electric vehicle</atitle><jtitle>International journal of circuit theory and applications</jtitle><date>2021-11</date><risdate>2021</risdate><volume>49</volume><issue>11</issue><spage>3864</spage><epage>3878</epage><pages>3864-3878</pages><issn>0098-9886</issn><eissn>1097-007X</eissn><abstract>This paper presents a model predictive control (MPC) approach for energy management of a hybrid energy storage system (HESS), in an electric vehicle (EV). HESS constitutes the battery and the supercapacitor (SC) where the latter is used as an auxiliary source to reduce stress on the battery. Hence, an appropriate control strategy should be formulated for allocating low‐frequency power fluctuations to the battery and high‐frequency power fluctuations to SC. The conventional PI‐based control strategy has a difficult tuning process, and its performance is affected when the operating point fluctuates. Therefore, an MPC‐based control strategy is proposed because of its simplicity, intuitiveness, ease of implementation, and inclusion of nonlinearities and constraints. This paper provides a complete power management strategy for the HESS system, using a modified MPC approach which is simpler compared to the traditional MPC, for a multiple‐input bidirectional DC/DC converter. Firstly, the model of a dual input bidirectional DC/DC converter is developed, and a two‐loop control strategy is developed with predictive inner current control and outer voltage control for DC‐link voltage regulation and HESS energy management. Secondly, an SC voltage regulation loop is developed for enabling the charging and discharging of SC for reliable operation of the HESS system. Finally, simulation studies are done using MATLAB Simulink, and a prototype experimental setup is developed to validate the effectiveness of the proposed control strategy through the dSPACE platform.
A model predictive control‐based control strategy is developed for a multiple‐input bidirectional DC/DC converter with a hybrid energy storage system of an electric vehicle. The proposed control strategy is compared with the conventional PID‐based strategy to validate the effectiveness of the proposed strategy in terms of faster DC‐link voltage regulation and complete control of battery discharge rate. The proposed strategy reduces stress on the battery and ensures the reliable operation of the converter with supercapacitor voltage regulation.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/cta.3119</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-9426-5189</orcidid></addata></record> |
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subjects | Batteries bidirectional DC/DC converter Electric converters Electric potential electric vehicle (EV) Electric vehicles Energy management Energy storage hybrid energy storage system (HESS) Hybrid systems model predictive control (MPC) Power management Predictive control Voltage |
title | A predictive power management scheme for hybrid energy storage system in electric vehicle |
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