Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side
<p dir="ltr">Inverse model predictive control (IMPC) is a control technique that was recently proposed for power electronic converters. IMPC inherits the advantages of model predictive control (MPC) in terms of ability to handle complex and nonlinear systems and achieving multiple co...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513541077204992 |
|---|---|
| author | Ali Sharida (17947847) |
| author2 | Abdullah Berkay Bayindir (22048088) Sertac Bayhan (16388511) Haitham Abu-Rub (16855500) |
| author2_role | author author author |
| author_facet | Ali Sharida (17947847) Abdullah Berkay Bayindir (22048088) Sertac Bayhan (16388511) Haitham Abu-Rub (16855500) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali Sharida (17947847) Abdullah Berkay Bayindir (22048088) Sertac Bayhan (16388511) Haitham Abu-Rub (16855500) |
| dc.date.none.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojies.2024.3435862 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhanced_Inverse_Model_Predictive_Control_for_EV_Chargers_Solution_for_Rectifier-Side/29900807 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Adaptive control bidirectional power flow electric vehicle (EV) chargers grid-to-vehicle (G2V) inverse model predictive control (IMPC) multilevel converters T-type rectifiers vehicle-to-grid (V2G) |
| dc.title.none.fl_str_mv | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Inverse model predictive control (IMPC) is a control technique that was recently proposed for power electronic converters. IMPC inherits the advantages of model predictive control (MPC) in terms of ability to handle complex and nonlinear systems and achieving multiple control objectives, while adhering to various constraints. Unlike MPC, IMPC offers a significantly reduced computational burden by omitting the iterative computations of the cost functions and states predictions. Nevertheless, both IMPC and MPC rely significantly on the dynamic model of the power converter. This makes them susceptible to uncertainties and disturbances. This article presents a novel technique to enhance the reliability and robustness of the IMPC for electric vehicle chargers by treating the converter's dynamic model as a black box. Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. The key benefit of the proposed technique is the utilization of an accurate and real-time estimated dynamic model, which facilitates a reliable states prediction by the IMPC. The effectiveness of the proposed technique is demonstrated through extensive simulations and experimental validation for a three-phase three-level T-type rectifier.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Industrial Electronics Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojies.2024.3435862" target="_blank">https://dx.doi.org/10.1109/ojies.2024.3435862</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1dbb4328601ddc925dafdf72dfb361e1 |
| identifier_str_mv | 10.1109/ojies.2024.3435862 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29900807 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-SideAli Sharida (17947847)Abdullah Berkay Bayindir (22048088)Sertac Bayhan (16388511)Haitham Abu-Rub (16855500)EngineeringElectrical engineeringElectronics, sensors and digital hardwareAdaptive controlbidirectional power flowelectric vehicle (EV) chargersgrid-to-vehicle (G2V)inverse model predictive control (IMPC)multilevel convertersT-type rectifiersvehicle-to-grid (V2G)<p dir="ltr">Inverse model predictive control (IMPC) is a control technique that was recently proposed for power electronic converters. IMPC inherits the advantages of model predictive control (MPC) in terms of ability to handle complex and nonlinear systems and achieving multiple control objectives, while adhering to various constraints. Unlike MPC, IMPC offers a significantly reduced computational burden by omitting the iterative computations of the cost functions and states predictions. Nevertheless, both IMPC and MPC rely significantly on the dynamic model of the power converter. This makes them susceptible to uncertainties and disturbances. This article presents a novel technique to enhance the reliability and robustness of the IMPC for electric vehicle chargers by treating the converter's dynamic model as a black box. Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. The key benefit of the proposed technique is the utilization of an accurate and real-time estimated dynamic model, which facilitates a reliable states prediction by the IMPC. The effectiveness of the proposed technique is demonstrated through extensive simulations and experimental validation for a three-phase three-level T-type rectifier.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Industrial Electronics Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojies.2024.3435862" target="_blank">https://dx.doi.org/10.1109/ojies.2024.3435862</a></p>2024-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojies.2024.3435862https://figshare.com/articles/journal_contribution/Enhanced_Inverse_Model_Predictive_Control_for_EV_Chargers_Solution_for_Rectifier-Side/29900807CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299008072024-01-01T00:00:00Z |
| spellingShingle | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side Ali Sharida (17947847) Engineering Electrical engineering Electronics, sensors and digital hardware Adaptive control bidirectional power flow electric vehicle (EV) chargers grid-to-vehicle (G2V) inverse model predictive control (IMPC) multilevel converters T-type rectifiers vehicle-to-grid (V2G) |
| status_str | publishedVersion |
| title | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| title_full | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| title_fullStr | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| title_full_unstemmed | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| title_short | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| title_sort | Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Adaptive control bidirectional power flow electric vehicle (EV) chargers grid-to-vehicle (G2V) inverse model predictive control (IMPC) multilevel converters T-type rectifiers vehicle-to-grid (V2G) |