DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger
A DSP-based probabilistic fuzzy neural network (PFNN) controller to control a two-stage ac-dc charger is pro- posed in this study. The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a...
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| Veröffentlicht in: | IEEE transactions on power electronics 2012-08, Vol.27 (8), p.3782-3794 |
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| container_title | IEEE transactions on power electronics |
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| creator | LIN, Faa-Jeng HUANG, Ming-Shi YEH, Po-Yi TSAI, Han-Chang KUAN, Chi-Hsuan |
| description | A DSP-based probabilistic fuzzy neural network (PFNN) controller to control a two-stage ac-dc charger is pro- posed in this study. The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a constant-current and constant-voltage (CC-CV) charging strategy to charge lithium-ion battery packs. To improve the transient of voltage regulation during load variation, a PFNN controller is pro- posed to replace the traditional proportional-integral controller. Furthermore, the discontinuous charging voltage and current during the transition between the CC and CV charging modes can also be reduced significantly using the proposed PFNN controller. The network structure and the online learning algorithms of the PFNN controller are introduced in detail. In addition, the control performances of the proposed PFNN control system for CC-CV charging are evaluated by experimental results. |
| doi_str_mv | 10.1109/TPEL.2012.2187073 |
| format | Article |
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The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a constant-current and constant-voltage (CC-CV) charging strategy to charge lithium-ion battery packs. To improve the transient of voltage regulation during load variation, a PFNN controller is pro- posed to replace the traditional proportional-integral controller. Furthermore, the discontinuous charging voltage and current during the transition between the CC and CV charging modes can also be reduced significantly using the proposed PFNN controller. The network structure and the online learning algorithms of the PFNN controller are introduced in detail. In addition, the control performances of the proposed PFNN control system for CC-CV charging are evaluated by experimental results.</description><identifier>ISSN: 0885-8993</identifier><identifier>EISSN: 1941-0107</identifier><identifier>DOI: 10.1109/TPEL.2012.2187073</identifier><identifier>CODEN: ITPEE8</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>AC-DC power converters ; Algorithms ; Applied sciences ; Batteries ; Constant-current (CC) charging ; constant-voltage (CV) charging DSP ; Control systems ; Controllers ; Electric currents ; Electric, optical and optoelectronic circuits ; Electrical engineering. Electrical power engineering ; Electrical machines ; Electronics ; Exact sciences and technology ; Fuzzy control ; Fuzzy neural networks ; Integrated circuits ; Integrated circuits by function (including memories and processors) ; Miscellaneous ; Neural networks ; phase-shift full-bridge (PSFB) ; power factor correction (PFC) ; probabilistic fuzzy neural network (PFNN) ; Probabilistic logic ; Regulation and control ; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices ; Various equipment and components ; Voltage control</subject><ispartof>IEEE transactions on power electronics, 2012-08, Vol.27 (8), p.3782-3794</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a constant-current and constant-voltage (CC-CV) charging strategy to charge lithium-ion battery packs. To improve the transient of voltage regulation during load variation, a PFNN controller is pro- posed to replace the traditional proportional-integral controller. Furthermore, the discontinuous charging voltage and current during the transition between the CC and CV charging modes can also be reduced significantly using the proposed PFNN controller. The network structure and the online learning algorithms of the PFNN controller are introduced in detail. In addition, the control performances of the proposed PFNN control system for CC-CV charging are evaluated by experimental results.</description><subject>AC-DC power converters</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Batteries</subject><subject>Constant-current (CC) charging</subject><subject>constant-voltage (CV) charging DSP</subject><subject>Control systems</subject><subject>Controllers</subject><subject>Electric currents</subject><subject>Electric, optical and optoelectronic circuits</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical machines</subject><subject>Electronics</subject><subject>Exact sciences and technology</subject><subject>Fuzzy control</subject><subject>Fuzzy neural networks</subject><subject>Integrated circuits</subject><subject>Integrated circuits by function (including memories and processors)</subject><subject>Miscellaneous</subject><subject>Neural networks</subject><subject>phase-shift full-bridge (PSFB)</subject><subject>power factor correction (PFC)</subject><subject>probabilistic fuzzy neural network (PFNN)</subject><subject>Probabilistic logic</subject><subject>Regulation and control</subject><subject>Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices</subject><subject>Various equipment and components</subject><subject>Voltage control</subject><issn>0885-8993</issn><issn>1941-0107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE9Lw0AQxRdRsEY_gHgJiMfU2ezfHG1ttRBswXoOm3RXU2O27iZI--nd0tLLPJh582b4IXSLYYgxZI_LxSQfpoDTYYqlAEHO0ABnFCeAQZyjAUjJEpll5BJdeb8GwJQBHqD58_siGSmvV_HC2VKVdVP7rq7iab_bbeM33TvVBOn-rPuOx7btnG1iY12c18nMtvFIdZ1223j8pdyndtfowqjG65ujRuhjOlmOX5N8_jIbP-VJRYnoEkYErSQDCiLTXAEjoRPelUC0MaB0RbVQJc1KzQlRkkojVoyULFUgRGpIhO4PuRtnf3vtu2Jte9eGkwUGyDjjNIRGCB9clbPeO22Kjat_lNsGU7HnVuy5FXtuxZFb2Hk4JitfqcY41Va1Py2mHBNOQ4nQ3cFXa61PY46pTCUn_32DdCI</recordid><startdate>20120801</startdate><enddate>20120801</enddate><creator>LIN, Faa-Jeng</creator><creator>HUANG, Ming-Shi</creator><creator>YEH, Po-Yi</creator><creator>TSAI, Han-Chang</creator><creator>KUAN, Chi-Hsuan</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Electrical power engineering</topic><topic>Electrical machines</topic><topic>Electronics</topic><topic>Exact sciences and technology</topic><topic>Fuzzy control</topic><topic>Fuzzy neural networks</topic><topic>Integrated circuits</topic><topic>Integrated circuits by function (including memories and processors)</topic><topic>Miscellaneous</topic><topic>Neural networks</topic><topic>phase-shift full-bridge (PSFB)</topic><topic>power factor correction (PFC)</topic><topic>probabilistic fuzzy neural network (PFNN)</topic><topic>Probabilistic logic</topic><topic>Regulation and control</topic><topic>Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices</topic><topic>Various equipment and components</topic><topic>Voltage control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>LIN, Faa-Jeng</creatorcontrib><creatorcontrib>HUANG, Ming-Shi</creatorcontrib><creatorcontrib>YEH, Po-Yi</creatorcontrib><creatorcontrib>TSAI, Han-Chang</creatorcontrib><creatorcontrib>KUAN, Chi-Hsuan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on power electronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIN, Faa-Jeng</au><au>HUANG, Ming-Shi</au><au>YEH, Po-Yi</au><au>TSAI, Han-Chang</au><au>KUAN, Chi-Hsuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger</atitle><jtitle>IEEE transactions on power electronics</jtitle><stitle>TPEL</stitle><date>2012-08-01</date><risdate>2012</risdate><volume>27</volume><issue>8</issue><spage>3782</spage><epage>3794</epage><pages>3782-3794</pages><issn>0885-8993</issn><eissn>1941-0107</eissn><coden>ITPEE8</coden><abstract>A DSP-based probabilistic fuzzy neural network (PFNN) controller to control a two-stage ac-dc charger is pro- posed in this study. The charger is composed of an ac-dc boost converter with power factor correction and a phase-shift full- bridge dc-dc converter. Moreover, the designed charger adopts a constant-current and constant-voltage (CC-CV) charging strategy to charge lithium-ion battery packs. To improve the transient of voltage regulation during load variation, a PFNN controller is pro- posed to replace the traditional proportional-integral controller. Furthermore, the discontinuous charging voltage and current during the transition between the CC and CV charging modes can also be reduced significantly using the proposed PFNN controller. The network structure and the online learning algorithms of the PFNN controller are introduced in detail. In addition, the control performances of the proposed PFNN control system for CC-CV charging are evaluated by experimental results.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TPEL.2012.2187073</doi><tpages>13</tpages></addata></record> |
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| subjects | AC-DC power converters Algorithms Applied sciences Batteries Constant-current (CC) charging constant-voltage (CV) charging DSP Control systems Controllers Electric currents Electric, optical and optoelectronic circuits Electrical engineering. Electrical power engineering Electrical machines Electronics Exact sciences and technology Fuzzy control Fuzzy neural networks Integrated circuits Integrated circuits by function (including memories and processors) Miscellaneous Neural networks phase-shift full-bridge (PSFB) power factor correction (PFC) probabilistic fuzzy neural network (PFNN) Probabilistic logic Regulation and control Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices Various equipment and components Voltage control |
| title | DSP-Based Probabilistic Fuzzy Neural Network Control for Li-Ion Battery Charger |
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