Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles

Qian Zhang; Yang Shi; Kui Wu

doi:10.1109/ICPS49255.2021.9468155

Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles

Qian Zhang
, Yang Shi
, Kui Wu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Plug-in electric vehicles (PEVs) charging process significantly impacts the efficiency and even the safety of the electrical power grid. In this work, we study a decentralized framework of PEVs charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed for achieving the valley-filling performance. The design of the decentralized model predictive control meets the individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Simulation results demonstrate the performance and verify the effectiveness of the proposed framework. © 2021 IEEE.

Original language	English
Title of host publication	Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)
Publisher	IEEE
Pages	341-346
ISBN (Electronic)	978-1-7281-6207-2
ISBN (Print)	978-1-6654-3045-6
DOIs	https://doi.org/10.1109/ICPS49255.2021.9468155
Publication status	Published - 2021
Externally published	Yes
Event	4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021) - Online, Canada Duration: 10 May 2021 → 13 May 2021 https://ieeexplore.ieee.org/xpl/conhome/9467798/proceeding

Publication series

Name	Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS

Conference

Conference	4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021)
Place	Canada
Period	10/05/21 → 13/05/21
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9467798/proceeding

UN SDGs

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

SDG 7 Affordable and Clean Energy

Research Keywords

Decentralized control
Iterative learning control
Model predictive control
Plug-in electric vehicles charging
Valley-filling

Access Output

10.1109/ICPS49255.2021.9468155

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Cite this

Zhang, Q., Shi, Y., & Wu, K. (2021). Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles. In Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 341-346). (Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS). IEEE. https://doi.org/10.1109/ICPS49255.2021.9468155

@inproceedings{4669de5bae4641f2bd30cdbe68d154eb,

title = "Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles",

abstract = "Plug-in electric vehicles (PEVs) charging process significantly impacts the efficiency and even the safety of the electrical power grid. In this work, we study a decentralized framework of PEVs charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed for achieving the valley-filling performance. The design of the decentralized model predictive control meets the individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Simulation results demonstrate the performance and verify the effectiveness of the proposed framework. {\textcopyright} 2021 IEEE.",

keywords = "Decentralized control, Iterative learning control, Model predictive control, Plug-in electric vehicles charging, Valley-filling",

author = "Qian Zhang and Yang Shi and Kui Wu",

year = "2021",

doi = "10.1109/ICPS49255.2021.9468155",

language = "English",

isbn = "978-1-6654-3045-6",

series = "Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS",

publisher = "IEEE",

pages = "341--346",

booktitle = "Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)",

address = "United States",

note = "4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021) ; Conference date: 10-05-2021 Through 13-05-2021",

url = "https://ieeexplore.ieee.org/xpl/conhome/9467798/proceeding",

}

Zhang, Q, Shi, Y & Wu, K 2021, Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles. in Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS, IEEE, pp. 341-346, 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021), Canada, 10/05/21. https://doi.org/10.1109/ICPS49255.2021.9468155

Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles. / Zhang, Qian; Shi, Yang; Wu, Kui.
Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE, 2021. p. 341-346 (Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles

AU - Zhang, Qian

AU - Shi, Yang

AU - Wu, Kui

PY - 2021

Y1 - 2021

N2 - Plug-in electric vehicles (PEVs) charging process significantly impacts the efficiency and even the safety of the electrical power grid. In this work, we study a decentralized framework of PEVs charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed for achieving the valley-filling performance. The design of the decentralized model predictive control meets the individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Simulation results demonstrate the performance and verify the effectiveness of the proposed framework. © 2021 IEEE.

AB - Plug-in electric vehicles (PEVs) charging process significantly impacts the efficiency and even the safety of the electrical power grid. In this work, we study a decentralized framework of PEVs charging problem with a coordination task. An iterative learning-based model predictive charging control algorithm is developed for achieving the valley-filling performance. The design of the decentralized model predictive control meets the individual charging requirements. The iterative learning method approximates the electricity price function and the system state sampled safe set to improve the accuracy of optimization problem calculations. The decentralized problem, in which individual PEV minimizes its own charging cost, is formulated based on the sum of all power loads. Simulation results demonstrate the performance and verify the effectiveness of the proposed framework. © 2021 IEEE.

KW - Decentralized control

KW - Iterative learning control

KW - Model predictive control

KW - Plug-in electric vehicles charging

KW - Valley-filling

UR - https://www.scopus.com/pages/publications/85112389633

UR - https://www.scopus.com/pages/publications/85112389633#tab=impact

U2 - 10.1109/ICPS49255.2021.9468155

DO - 10.1109/ICPS49255.2021.9468155

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-6654-3045-6

T3 - Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS

SP - 341

EP - 346

BT - Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

PB - IEEE

T2 - 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2021)

Y2 - 10 May 2021 through 13 May 2021

ER -

Zhang Q, Shi Y, Wu K. Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles. In Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE. 2021. p. 341-346. (Proceedings - IEEE International Conference on Industrial Cyber-Physical Systems, ICPS). Epub 2021 Jul 5. doi: 10.1109/ICPS49255.2021.9468155

Iterative learning-based decentralized model predictive charging control for plug-in electric vehicles

Abstract

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UN SDGs

Research Keywords

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