Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management

Xiaopeng TANG; Xin LAI; Yuanqiang ZHOU; Ming YUAN; Furong GAO

doi:10.1007/978-981-99-1027-4_133

Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management

Xiaopeng TANG^*, Xin LAI, Yuanqiang ZHOU, Ming YUAN, Furong GAO

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

1 Citation (Scopus)

Abstract

The fractional-order model is effective in simulating the battery’s dynamic behaviour. However, the real-time implementation of a fractional-order component can be complicated as the related Grünwald–Letnikov (G–L) definition involves sophisticated cumulative operations. Here, we propose to use an integer-order model with frequency-dependent parameters to approximate the fractional order model. Its implementation is described as follows: In the frequency domain, multiple third-order RC models are switched with frequency to depict the battery’s impedance spectroscopy. In the time domain, our model is transformed into a weighted summation of multiple third-order RC models, whose weighting factors are determined by the current signal’s energy in the frequency ranges of interest. The comparison between the integer order RC model, the proposed frequency-dependent RC model, and the fractional order model is carried out in both the time and frequency domain to verify the effectiveness of the proposed method. The computational effort of the proposed model can be significantly reduced by 85% compared with the fractional order model, and the modelling error is reduced by 51% compared with the conventional integer order model. Our model provides an accurate yet computationally efficient way to describe the battery’s dynamic.

Original language	English
Title of host publication	The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022
Editors	Fengchun SUN, Qingxin YANG, Erik DAHLQUIST, Rui XIONG
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1276-1284
Number of pages	9
ISBN (Print)	9789819910267
DOIs	https://doi.org/10.1007/978-981-99-1027-4_133
Publication status	Published - 2023
Externally published	Yes
Event	5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 - Virtual, Online Duration: 3 Dec 2022 → 4 Dec 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Publisher	Springer
Volume	1016
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022
City	Virtual, Online
Period	3/12/22 → 4/12/22

Bibliographical note

Funding Information:
This work is supported by the Guangdong scientific and technological project (2019A050516002), Guangzhou Scientific and Technological Project (202002030323), and the National Natural Science Foundation of China (51977131). The first author is an awardee of the Hong Kong RGC Postdoctoral Fellowship (PDFS2122-6S06).

Publisher Copyright:
© 2023, Beijing Paike Culture Commu. Co., Ltd.

Keywords

Fractional-order model
Frequency-dependent model
Integer-order model

Access to Document

10.1007/978-981-99-1027-4_133

Cite this

TANG, X., LAI, X., ZHOU, Y., YUAN, M., & GAO, F. (2023). Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management. In F. SUN, Q. YANG, E. DAHLQUIST, & R. XIONG (Eds.), The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 (pp. 1276-1284). (Lecture Notes in Electrical Engineering; Vol. 1016). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-1027-4_133

TANG, Xiaopeng ; LAI, Xin ; ZHOU, Yuanqiang et al. / Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management. The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. editor / Fengchun SUN ; Qingxin YANG ; Erik DAHLQUIST ; Rui XIONG. Springer Science and Business Media Deutschland GmbH, 2023. pp. 1276-1284 (Lecture Notes in Electrical Engineering).

@inproceedings{e885bb2f1bba4c009e7c10c600ead4c4,

title = "Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management",

abstract = "The fractional-order model is effective in simulating the battery{\textquoteright}s dynamic behaviour. However, the real-time implementation of a fractional-order component can be complicated as the related Gr{\"u}nwald–Letnikov (G–L) definition involves sophisticated cumulative operations. Here, we propose to use an integer-order model with frequency-dependent parameters to approximate the fractional order model. Its implementation is described as follows: In the frequency domain, multiple third-order RC models are switched with frequency to depict the battery{\textquoteright}s impedance spectroscopy. In the time domain, our model is transformed into a weighted summation of multiple third-order RC models, whose weighting factors are determined by the current signal{\textquoteright}s energy in the frequency ranges of interest. The comparison between the integer order RC model, the proposed frequency-dependent RC model, and the fractional order model is carried out in both the time and frequency domain to verify the effectiveness of the proposed method. The computational effort of the proposed model can be significantly reduced by 85% compared with the fractional order model, and the modelling error is reduced by 51% compared with the conventional integer order model. Our model provides an accurate yet computationally efficient way to describe the battery{\textquoteright}s dynamic.",

keywords = "Fractional-order model, Frequency-dependent model, Integer-order model",

author = "Xiaopeng TANG and Xin LAI and Yuanqiang ZHOU and Ming YUAN and Furong GAO",

note = "Funding Information: This work is supported by the Guangdong scientific and technological project (2019A050516002), Guangzhou Scientific and Technological Project (202002030323), and the National Natural Science Foundation of China (51977131). The first author is an awardee of the Hong Kong RGC Postdoctoral Fellowship (PDFS2122-6S06). Publisher Copyright: {\textcopyright} 2023, Beijing Paike Culture Commu. Co., Ltd.; 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022 ; Conference date: 03-12-2022 Through 04-12-2022",

year = "2023",

doi = "10.1007/978-981-99-1027-4_133",

language = "English",

isbn = "9789819910267",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "1276--1284",

editor = "Fengchun SUN and Qingxin YANG and Erik DAHLQUIST and Rui XIONG",

booktitle = "The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022",

address = "Germany",

}

TANG, X, LAI, X, ZHOU, Y, YUAN, M & GAO, F 2023, Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management. in F SUN, Q YANG, E DAHLQUIST & R XIONG (eds), The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. Lecture Notes in Electrical Engineering, vol. 1016, Springer Science and Business Media Deutschland GmbH, pp. 1276-1284, 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022, Virtual, Online, 3/12/22. https://doi.org/10.1007/978-981-99-1027-4_133

Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management. / TANG, Xiaopeng; LAI, Xin; ZHOU, Yuanqiang et al.
The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. ed. / Fengchun SUN; Qingxin YANG; Erik DAHLQUIST; Rui XIONG. Springer Science and Business Media Deutschland GmbH, 2023. p. 1276-1284 (Lecture Notes in Electrical Engineering; Vol. 1016).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management

AU - TANG, Xiaopeng

AU - LAI, Xin

AU - ZHOU, Yuanqiang

AU - YUAN, Ming

AU - GAO, Furong

N1 - Funding Information: This work is supported by the Guangdong scientific and technological project (2019A050516002), Guangzhou Scientific and Technological Project (202002030323), and the National Natural Science Foundation of China (51977131). The first author is an awardee of the Hong Kong RGC Postdoctoral Fellowship (PDFS2122-6S06). Publisher Copyright: © 2023, Beijing Paike Culture Commu. Co., Ltd.

PY - 2023

Y1 - 2023

N2 - The fractional-order model is effective in simulating the battery’s dynamic behaviour. However, the real-time implementation of a fractional-order component can be complicated as the related Grünwald–Letnikov (G–L) definition involves sophisticated cumulative operations. Here, we propose to use an integer-order model with frequency-dependent parameters to approximate the fractional order model. Its implementation is described as follows: In the frequency domain, multiple third-order RC models are switched with frequency to depict the battery’s impedance spectroscopy. In the time domain, our model is transformed into a weighted summation of multiple third-order RC models, whose weighting factors are determined by the current signal’s energy in the frequency ranges of interest. The comparison between the integer order RC model, the proposed frequency-dependent RC model, and the fractional order model is carried out in both the time and frequency domain to verify the effectiveness of the proposed method. The computational effort of the proposed model can be significantly reduced by 85% compared with the fractional order model, and the modelling error is reduced by 51% compared with the conventional integer order model. Our model provides an accurate yet computationally efficient way to describe the battery’s dynamic.

AB - The fractional-order model is effective in simulating the battery’s dynamic behaviour. However, the real-time implementation of a fractional-order component can be complicated as the related Grünwald–Letnikov (G–L) definition involves sophisticated cumulative operations. Here, we propose to use an integer-order model with frequency-dependent parameters to approximate the fractional order model. Its implementation is described as follows: In the frequency domain, multiple third-order RC models are switched with frequency to depict the battery’s impedance spectroscopy. In the time domain, our model is transformed into a weighted summation of multiple third-order RC models, whose weighting factors are determined by the current signal’s energy in the frequency ranges of interest. The comparison between the integer order RC model, the proposed frequency-dependent RC model, and the fractional order model is carried out in both the time and frequency domain to verify the effectiveness of the proposed method. The computational effort of the proposed model can be significantly reduced by 85% compared with the fractional order model, and the modelling error is reduced by 51% compared with the conventional integer order model. Our model provides an accurate yet computationally efficient way to describe the battery’s dynamic.

KW - Fractional-order model

KW - Frequency-dependent model

KW - Integer-order model

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U2 - 10.1007/978-981-99-1027-4_133

DO - 10.1007/978-981-99-1027-4_133

M3 - Conference paper (refereed)

AN - SCOPUS:85161137281

SN - 9789819910267

T3 - Lecture Notes in Electrical Engineering

SP - 1276

EP - 1284

BT - The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

A2 - SUN, Fengchun

A2 - YANG, Qingxin

A2 - DAHLQUIST, Erik

A2 - XIONG, Rui

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022

Y2 - 3 December 2022 through 4 December 2022

ER -

TANG X, LAI X, ZHOU Y, YUAN M, GAO F. Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management. In SUN F, YANG Q, DAHLQUIST E, XIONG R, editors, The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles, ICEIV 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 1276-1284. (Lecture Notes in Electrical Engineering). Epub 2023 May 11. doi: 10.1007/978-981-99-1027-4_133

Using Frequency-Dependent Integer Order Models to Simulate Fractional Order Model for Battery Management

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