Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side
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 adher...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Industrial Electronics Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10614823/ |
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| author | Ali Sharida Abdullah Berkay Bayindir Sertac Bayhan Haitham Abu-Rub |
| author_facet | Ali Sharida Abdullah Berkay Bayindir Sertac Bayhan Haitham Abu-Rub |
| author_sort | Ali Sharida |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-118c4e90b5f341dc9ea76af06117f9fa |
| institution | Kabale University |
| issn | 2644-1284 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Industrial Electronics Society |
| spelling | doaj-art-118c4e90b5f341dc9ea76af06117f9fa2025-01-17T00:00:45ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842024-01-01579580610.1109/OJIES.2024.343586210614823Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-SideAli Sharida0https://orcid.org/0000-0001-6954-7192Abdullah Berkay Bayindir1Sertac Bayhan2https://orcid.org/0000-0003-2027-532XHaitham Abu-Rub3https://orcid.org/0000-0001-8687-3942Texas A&M University, Doha, QatarTexas A&M University, Doha, QatarQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Ar-Rayyan, QatarTexas A&M University, College Station, TX, USAInverse 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.https://ieeexplore.ieee.org/document/10614823/Adaptive controlbidirectional power flowelectric vehicle (EV) chargersgrid-to-vehicle (G2V)inverse model predictive control (IMPC)multilevel converters |
| spellingShingle | Ali Sharida Abdullah Berkay Bayindir Sertac Bayhan Haitham Abu-Rub Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side IEEE Open Journal of the Industrial Electronics Society Adaptive control bidirectional power flow electric vehicle (EV) chargers grid-to-vehicle (G2V) inverse model predictive control (IMPC) multilevel converters |
| 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 | Adaptive control bidirectional power flow electric vehicle (EV) chargers grid-to-vehicle (G2V) inverse model predictive control (IMPC) multilevel converters |
| url | https://ieeexplore.ieee.org/document/10614823/ |
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