Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side

This article presents an approach for enhancing the reliability and robustness of electric vehicle (EV) chargers, particularly the dc–dc side of the EV chargers, by using the inverse model predictive control (IMPC). IMPC, a recently introduced control method for power electronic converter...

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Main Authors: Abdullah Berkay Bayindir, Ali Sharida, Sertac Bayhan, Haitham Abu-Rub
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of the Industrial Electronics Society
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10935818/
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author Abdullah Berkay Bayindir
Ali Sharida
Sertac Bayhan
Haitham Abu-Rub
author_facet Abdullah Berkay Bayindir
Ali Sharida
Sertac Bayhan
Haitham Abu-Rub
author_sort Abdullah Berkay Bayindir
collection DOAJ
description This article presents an approach for enhancing the reliability and robustness of electric vehicle (EV) chargers, particularly the dc–dc side of the EV chargers, by using the inverse model predictive control (IMPC). IMPC, a recently introduced control method for power electronic converters, leverages the strengths of model predictive control (MPC), while minimizing its computational burden. IMPC excels in managing sophisticated and nonlinear systems, controlling multiple objectives, and adhering to various constraints. However, the effectiveness of conventional IMPC is heavily dependent on the accurate dynamic model of the power converter. This dependency makes IMPC susceptible to uncertainties and disturbances. To address this challenge, the proposed method employs an adaptive estimation strategy utilizing a recursive least square algorithm for online dynamic model estimation. This real-time estimated model enables IMPC to predict optimal switching states with improved reliability. The proposed control technique is designed to provide constant power, constant current, and constant voltage modes, with the ability to seamlessly transition between them. The efficacy of this technique is demonstrated through extensive simulations and experimental validation for a dual active bridge (DAB) converter. This adaptive method underscores the potential of IMPC for practical EV charging scenarios, ensuring reliable and high-performance charging.
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institution OA Journals
issn 2644-1284
language English
publishDate 2025-01-01
publisher IEEE
record_format Article
series IEEE Open Journal of the Industrial Electronics Society
spelling doaj-art-6240fe9b488f4067ba2ef333df6e377f2025-08-20T01:54:34ZengIEEEIEEE Open Journal of the Industrial Electronics Society2644-12842025-01-01647849010.1109/OJIES.2025.355306110935818Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC SideAbdullah Berkay Bayindir0https://orcid.org/0000-0001-9355-8609Ali Sharida1https://orcid.org/0000-0001-6954-7192Sertac Bayhan2https://orcid.org/0000-0003-2027-532XHaitham Abu-Rub3https://orcid.org/0000-0001-8687-3942Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USAQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, QatarQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, QatarDepartment of Electrical Engineering, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarThis article presents an approach for enhancing the reliability and robustness of electric vehicle (EV) chargers, particularly the dc–dc side of the EV chargers, by using the inverse model predictive control (IMPC). IMPC, a recently introduced control method for power electronic converters, leverages the strengths of model predictive control (MPC), while minimizing its computational burden. IMPC excels in managing sophisticated and nonlinear systems, controlling multiple objectives, and adhering to various constraints. However, the effectiveness of conventional IMPC is heavily dependent on the accurate dynamic model of the power converter. This dependency makes IMPC susceptible to uncertainties and disturbances. To address this challenge, the proposed method employs an adaptive estimation strategy utilizing a recursive least square algorithm for online dynamic model estimation. This real-time estimated model enables IMPC to predict optimal switching states with improved reliability. The proposed control technique is designed to provide constant power, constant current, and constant voltage modes, with the ability to seamlessly transition between them. The efficacy of this technique is demonstrated through extensive simulations and experimental validation for a dual active bridge (DAB) converter. This adaptive method underscores the potential of IMPC for practical EV charging scenarios, ensuring reliable and high-performance charging.https://ieeexplore.ieee.org/document/10935818/Adaptive controlbidirectional power flowdual active bridge (DAB)electric vehicle (EV) chargersgrid-to-vehicle (G2V)inverse model predictive control (IMPC)
spellingShingle Abdullah Berkay Bayindir
Ali Sharida
Sertac Bayhan
Haitham Abu-Rub
Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side
IEEE Open Journal of the Industrial Electronics Society
Adaptive control
bidirectional power flow
dual active bridge (DAB)
electric vehicle (EV) chargers
grid-to-vehicle (G2V)
inverse model predictive control (IMPC)
title Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side
title_full Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side
title_fullStr Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side
title_full_unstemmed Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side
title_short Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side
title_sort enhanced inverse model predictive control for ev chargers solution for dc x2013 dc side
topic Adaptive control
bidirectional power flow
dual active bridge (DAB)
electric vehicle (EV) chargers
grid-to-vehicle (G2V)
inverse model predictive control (IMPC)
url https://ieeexplore.ieee.org/document/10935818/
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AT alisharida enhancedinversemodelpredictivecontrolforevchargerssolutionfordcx2013dcside
AT sertacbayhan enhancedinversemodelpredictivecontrolforevchargerssolutionfordcx2013dcside
AT haithamaburub enhancedinversemodelpredictivecontrolforevchargerssolutionfordcx2013dcside