Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer

Yong Feng; Yanmin Wang; Fan Bai; Zhenwei Cao; Fengling Han

doi:10.1109/IECON55916.2024.10905812

Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer

Yong Feng^*
, Yanmin Wang
, Fan Bai
, Zhenwei Cao
, Fengling Han

^*Corresponding author for this work

School of Electrical Engineering and Automation, Harbin Institute of Technology
Swinburne University of Technology
Royal Melbourne Institute of Technology University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The state of energy (SoE) is a critical metric for battery management systems, complementing the state of charge (SoC) in providing comprehensive battery management insights. This paper addresses SoE estimation for lithium-ion batteries by designing a sliding mode observer that utilizes sliding mode control technology to enhance the robustness of the SoE estimation algorithm. Leveraging the correlation between SoC and SoE, this study develops a method to accurately estimate SoC and determine SoE. Additionally, we introduce a sliding mode observer based on the Super-twisting algorithm, which stabilizes the observer by carefully selecting parameters. The effectiveness of the proposed SoE estimation algorithm is validated using Dynamic Stress Test (DST) data, confirming its practical utility.

Original language	English
Title of host publication	IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665464543
DOIs	https://doi.org/10.1109/IECON55916.2024.10905812
State	Published - 2024
Externally published	Yes
Event	50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 - Chicago, United States Duration: 3 Nov 2024 → 6 Nov 2024

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024
Country/Territory	United States
City	Chicago
Period	3/11/24 → 6/11/24

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Lithium-ion battery
sliding-mode control (SMC)
state of charge(SoC)
state of energy (SoE)

Access to Document

10.1109/IECON55916.2024.10905812

Cite this

Feng, Y., Wang, Y., Bai, F., Cao, Z., & Han, F. (2024). Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer. In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings (IECON Proceedings (Industrial Electronics Conference)). IEEE Computer Society. https://doi.org/10.1109/IECON55916.2024.10905812

@inproceedings{8d82f0859a9b40979918f462ded7eeed,

title = "Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer",

abstract = "The state of energy (SoE) is a critical metric for battery management systems, complementing the state of charge (SoC) in providing comprehensive battery management insights. This paper addresses SoE estimation for lithium-ion batteries by designing a sliding mode observer that utilizes sliding mode control technology to enhance the robustness of the SoE estimation algorithm. Leveraging the correlation between SoC and SoE, this study develops a method to accurately estimate SoC and determine SoE. Additionally, we introduce a sliding mode observer based on the Super-twisting algorithm, which stabilizes the observer by carefully selecting parameters. The effectiveness of the proposed SoE estimation algorithm is validated using Dynamic Stress Test (DST) data, confirming its practical utility.",

keywords = "Lithium-ion battery, sliding-mode control (SMC), state of charge(SoC), state of energy (SoE)",

author = "Yong Feng and Yanmin Wang and Fan Bai and Zhenwei Cao and Fengling Han",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024 ; Conference date: 03-11-2024 Through 06-11-2024",

year = "2024",

doi = "10.1109/IECON55916.2024.10905812",

language = "英语",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings",

address = "美国",

}

Feng, Y, Wang, Y, Bai, F, Cao, Z & Han, F 2024, Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer. in IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024, Chicago, United States, 3/11/24. https://doi.org/10.1109/IECON55916.2024.10905812

Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer. / Feng, Yong; Wang, Yanmin; Bai, Fan et al.
IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings. IEEE Computer Society, 2024. (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer

AU - Feng, Yong

AU - Wang, Yanmin

AU - Bai, Fan

AU - Cao, Zhenwei

AU - Han, Fengling

PY - 2024

Y1 - 2024

N2 - The state of energy (SoE) is a critical metric for battery management systems, complementing the state of charge (SoC) in providing comprehensive battery management insights. This paper addresses SoE estimation for lithium-ion batteries by designing a sliding mode observer that utilizes sliding mode control technology to enhance the robustness of the SoE estimation algorithm. Leveraging the correlation between SoC and SoE, this study develops a method to accurately estimate SoC and determine SoE. Additionally, we introduce a sliding mode observer based on the Super-twisting algorithm, which stabilizes the observer by carefully selecting parameters. The effectiveness of the proposed SoE estimation algorithm is validated using Dynamic Stress Test (DST) data, confirming its practical utility.

AB - The state of energy (SoE) is a critical metric for battery management systems, complementing the state of charge (SoC) in providing comprehensive battery management insights. This paper addresses SoE estimation for lithium-ion batteries by designing a sliding mode observer that utilizes sliding mode control technology to enhance the robustness of the SoE estimation algorithm. Leveraging the correlation between SoC and SoE, this study develops a method to accurately estimate SoC and determine SoE. Additionally, we introduce a sliding mode observer based on the Super-twisting algorithm, which stabilizes the observer by carefully selecting parameters. The effectiveness of the proposed SoE estimation algorithm is validated using Dynamic Stress Test (DST) data, confirming its practical utility.

KW - Lithium-ion battery

KW - sliding-mode control (SMC)

KW - state of charge(SoC)

KW - state of energy (SoE)

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DO - 10.1109/IECON55916.2024.10905812

M3 - 会议稿件

AN - SCOPUS:105000986793

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings

PB - IEEE Computer Society

T2 - 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

Y2 - 3 November 2024 through 6 November 2024

ER -

Feng Y, Wang Y, Bai F, Cao Z, Han F. Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer. In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings. IEEE Computer Society. 2024. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON55916.2024.10905812

Estimating State of Energy in Lithium-ion Batteries Using a Super-Twisting Algorithm-Based Sliding Mode Observer

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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