A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging

Tianlu Ma; C. Q. Jiang; Chen Chen; Yibo Wang; Jiayi Geng; Chi K. Tse

doi:10.1109/TIE.2023.3344844

A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging

Tianlu Ma, C. Q. Jiang^*, Chen Chen, Yibo Wang, Jiayi Geng, Chi K. Tse

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

21 Citations (Scopus)

26 Downloads (CityUHK Scholars)

Abstract

Dynamic wireless charging (DWC) technology can help alleviate the problem of short driving range for battery-powered vehicles. In this article, a model predictive control (MPC) is applied to the buck converter on the secondary side of a DWC system to address fast output fluctuations. This approach features a fast-dynamic response, and no communication link is required. To solve the key issue of MPC, which is the computational burden, a polynomial fitting method based on the parsing solution of the sampled-data model is proposed. The complex matrix exponential calculation is replaced by simple polynomial operations, and the optimal duty cycle can be calculated directly by solving a quadratic function. This significantly reduces the computational burden. A DWC experimental setup is constructed, and results show that the proposed MPC has a better dynamic performance compared to proportional-integral control. The adjustment time is only 140 μs (around seven switching cycles) when the reference voltage is stepping. Moreover, the computational burden for matrix calculation in two-step prediction can be reduced by 50.6% and 79.7% compared to the lookup table and Taylor series approximation, respectively. Meanwhile, MPC with current limitation is analyzed and demonstrates a neat spectrum, small ripple but large response time.

© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

Original language	English
Pages (from-to)	10402-10413
Number of pages	12
Journal	IEEE Transactions on Industrial Electronics
Volume	71
Issue number	9
Online published	1 Jan 2024
DOIs	https://doi.org/10.1109/TIE.2023.3344844
Publication status	Published - Sept 2024

Funding

This work was supported in part by the Science Technology and Innovation Committee of Shenzhen Municipality, China, under Grant SGDX20210823104003034, in part by the Natural Science Foundation of China, China, under Grant 52107011, in part by the Research Grants Council, Hong Kong SAR under ECS Grant 21200622, and in part by the City University of Hong Kong under TDG 6000791

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Dc–dc converters
dynamic wireless charging (DWC)
model predictive control (MPC)
sampled-data model
wireless power transfer (WPT)

Publisher's Copyright Statement

COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Ma, T., Jiang, C. Q., Chen, C., Wang, Y., Geng, J., & Tse, C. K. (2024). A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging. IEEE Transactions on Industrial Electronics, 71(9), 10402 – 10413. https://doi.org/10.1109/TIE.2023.3344844

RGC Funding Information

RGC-funded

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10.1109/TIE.2023.3344844

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1 Finished
1 Active

ECS: Enhancement of the Newly Developed Nanocrystalline Ribbon Bidirectional Wireless EV Charging System
JIANG, C. (Principal Investigator / Project Coordinator)
1/01/23 → …
Project: Research
TDG(CityU): Novel Extended Vision for Undergraduate Education on Energy Engineering: New BreedApplications of Wireless Power Conversion
JIANG, C. (Principal Investigator / Project Coordinator)
1/06/22 → 30/11/23
Project: Research

Cite this

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title = "A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging",

abstract = "Dynamic wireless charging (DWC) technology can help alleviate the problem of short driving range for battery-powered vehicles. In this article, a model predictive control (MPC) is applied to the buck converter on the secondary side of a DWC system to address fast output fluctuations. This approach features a fast-dynamic response, and no communication link is required. To solve the key issue of MPC, which is the computational burden, a polynomial fitting method based on the parsing solution of the sampled-data model is proposed. The complex matrix exponential calculation is replaced by simple polynomial operations, and the optimal duty cycle can be calculated directly by solving a quadratic function. This significantly reduces the computational burden. A DWC experimental setup is constructed, and results show that the proposed MPC has a better dynamic performance compared to proportional-integral control. The adjustment time is only 140 μs (around seven switching cycles) when the reference voltage is stepping. Moreover, the computational burden for matrix calculation in two-step prediction can be reduced by 50.6% and 79.7% compared to the lookup table and Taylor series approximation, respectively. Meanwhile, MPC with current limitation is analyzed and demonstrates a neat spectrum, small ripple but large response time.{\textcopyright} 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.",

keywords = "Dc–dc converters, dynamic wireless charging (DWC), model predictive control (MPC), sampled-data model, wireless power transfer (WPT)",

author = "Tianlu Ma and Jiang, {C. Q.} and Chen Chen and Yibo Wang and Jiayi Geng and Tse, {Chi K.}",

year = "2024",

month = sep,

doi = "10.1109/TIE.2023.3344844",

language = "English",

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pages = "10402--10413",

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T1 - A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging

AU - Ma, Tianlu

AU - Jiang, C. Q.

AU - Chen, Chen

AU - Wang, Yibo

AU - Geng, Jiayi

AU - Tse, Chi K.

PY - 2024/9

Y1 - 2024/9

N2 - Dynamic wireless charging (DWC) technology can help alleviate the problem of short driving range for battery-powered vehicles. In this article, a model predictive control (MPC) is applied to the buck converter on the secondary side of a DWC system to address fast output fluctuations. This approach features a fast-dynamic response, and no communication link is required. To solve the key issue of MPC, which is the computational burden, a polynomial fitting method based on the parsing solution of the sampled-data model is proposed. The complex matrix exponential calculation is replaced by simple polynomial operations, and the optimal duty cycle can be calculated directly by solving a quadratic function. This significantly reduces the computational burden. A DWC experimental setup is constructed, and results show that the proposed MPC has a better dynamic performance compared to proportional-integral control. The adjustment time is only 140 μs (around seven switching cycles) when the reference voltage is stepping. Moreover, the computational burden for matrix calculation in two-step prediction can be reduced by 50.6% and 79.7% compared to the lookup table and Taylor series approximation, respectively. Meanwhile, MPC with current limitation is analyzed and demonstrates a neat spectrum, small ripple but large response time.© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

AB - Dynamic wireless charging (DWC) technology can help alleviate the problem of short driving range for battery-powered vehicles. In this article, a model predictive control (MPC) is applied to the buck converter on the secondary side of a DWC system to address fast output fluctuations. This approach features a fast-dynamic response, and no communication link is required. To solve the key issue of MPC, which is the computational burden, a polynomial fitting method based on the parsing solution of the sampled-data model is proposed. The complex matrix exponential calculation is replaced by simple polynomial operations, and the optimal duty cycle can be calculated directly by solving a quadratic function. This significantly reduces the computational burden. A DWC experimental setup is constructed, and results show that the proposed MPC has a better dynamic performance compared to proportional-integral control. The adjustment time is only 140 μs (around seven switching cycles) when the reference voltage is stepping. Moreover, the computational burden for matrix calculation in two-step prediction can be reduced by 50.6% and 79.7% compared to the lookup table and Taylor series approximation, respectively. Meanwhile, MPC with current limitation is analyzed and demonstrates a neat spectrum, small ripple but large response time.© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

KW - Dc–dc converters

KW - dynamic wireless charging (DWC)

KW - model predictive control (MPC)

KW - sampled-data model

KW - wireless power transfer (WPT)

U2 - 10.1109/TIE.2023.3344844

DO - 10.1109/TIE.2023.3344844

M3 - RGC 21 - Publication in refereed journal

SN - 1557-9948

VL - 71

SP - 10402

EP - 10413

JO - IEEE Transactions on Industrial Electronics

JF - IEEE Transactions on Industrial Electronics

IS - 9

ER -

A Low Computational Burden Model Predictive Control for Dynamic Wireless Charging

Abstract

Funding

UN SDGs

Research Keywords

Publisher's Copyright Statement

RGC Funding Information

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Projects

ECS: Enhancement of the Newly Developed Nanocrystalline Ribbon Bidirectional Wireless EV Charging System

TDG(CityU): Novel Extended Vision for Undergraduate Education on Energy Engineering: New BreedApplications of Wireless Power Conversion

Cite this