Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer

Shuo Zhang; Xinghao Wang; Chengrui Li; Dianxun Xiao

doi:10.1109/IECON51785.2023.10311754

Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer

Shuo Zhang
, Xinghao Wang
, Chengrui Li
, Dianxun Xiao^*

^*Corresponding author for this work

The Hong Kong University of Science and Technology (Guangzhou)
Harbin Institute of Technology Shenzhen
HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute

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

11 Scopus citations

Abstract

In recent years, the development of Lithium-ion battery technology has promoted the utilization of battery management systems (BMS). In the BMS, developing a reliable state of charge (SOC) estimation method is essential for managing and controlling batteries. Most of the research in this area has focused on the design of model-based observers. This paper proposes the self-adaptive super twisting sliding mode observer (SASTSMO) and improved particle swarm optimization (IPSO) algorithm to estimate SOC, which could effectively suppress the system chattering. The SASTSMO can estimate total disturbances in battery model without chattering issues and then compensate them to enhance the accuracy of SOC. Furthermore, the adaptive algorithm can adjust the gain of the observer according to the error automatically, which improves the robustness of the algorithm. Finally, the feasibility of the proposed method is verified under different test conditions.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10311754
State	Published - 2023
Externally published	Yes
Event	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023

Publication series

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

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/Territory	Singapore
City	Singapore
Period	16/10/23 → 19/10/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Improved particle swarm optimization (IPSO)
lithium-ion battery
self-adaptive super twisting sliding mode observer (SASTSMO)
state of charge (SOC)

Access to Document

10.1109/IECON51785.2023.10311754

Cite this

@inproceedings{47f1fb7350bd4e6ca43db73c56c88da5,

title = "Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer",

abstract = "In recent years, the development of Lithium-ion battery technology has promoted the utilization of battery management systems (BMS). In the BMS, developing a reliable state of charge (SOC) estimation method is essential for managing and controlling batteries. Most of the research in this area has focused on the design of model-based observers. This paper proposes the self-adaptive super twisting sliding mode observer (SASTSMO) and improved particle swarm optimization (IPSO) algorithm to estimate SOC, which could effectively suppress the system chattering. The SASTSMO can estimate total disturbances in battery model without chattering issues and then compensate them to enhance the accuracy of SOC. Furthermore, the adaptive algorithm can adjust the gain of the observer according to the error automatically, which improves the robustness of the algorithm. Finally, the feasibility of the proposed method is verified under different test conditions.",

keywords = "Improved particle swarm optimization (IPSO), lithium-ion battery, self-adaptive super twisting sliding mode observer (SASTSMO), state of charge (SOC)",

author = "Shuo Zhang and Xinghao Wang and Chengrui Li and Dianxun Xiao",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10311754",

language = "英语",

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

publisher = "IEEE Computer Society",

booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

address = "美国",

}

Zhang, S, Wang, X, Li, C & Xiao, D 2023, Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, 16/10/23. https://doi.org/10.1109/IECON51785.2023.10311754

Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer. / Zhang, Shuo; Wang, Xinghao; Li, Chengrui et al.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

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

TY - GEN

T1 - Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer

AU - Zhang, Shuo

AU - Wang, Xinghao

AU - Li, Chengrui

AU - Xiao, Dianxun

PY - 2023

Y1 - 2023

N2 - In recent years, the development of Lithium-ion battery technology has promoted the utilization of battery management systems (BMS). In the BMS, developing a reliable state of charge (SOC) estimation method is essential for managing and controlling batteries. Most of the research in this area has focused on the design of model-based observers. This paper proposes the self-adaptive super twisting sliding mode observer (SASTSMO) and improved particle swarm optimization (IPSO) algorithm to estimate SOC, which could effectively suppress the system chattering. The SASTSMO can estimate total disturbances in battery model without chattering issues and then compensate them to enhance the accuracy of SOC. Furthermore, the adaptive algorithm can adjust the gain of the observer according to the error automatically, which improves the robustness of the algorithm. Finally, the feasibility of the proposed method is verified under different test conditions.

AB - In recent years, the development of Lithium-ion battery technology has promoted the utilization of battery management systems (BMS). In the BMS, developing a reliable state of charge (SOC) estimation method is essential for managing and controlling batteries. Most of the research in this area has focused on the design of model-based observers. This paper proposes the self-adaptive super twisting sliding mode observer (SASTSMO) and improved particle swarm optimization (IPSO) algorithm to estimate SOC, which could effectively suppress the system chattering. The SASTSMO can estimate total disturbances in battery model without chattering issues and then compensate them to enhance the accuracy of SOC. Furthermore, the adaptive algorithm can adjust the gain of the observer according to the error automatically, which improves the robustness of the algorithm. Finally, the feasibility of the proposed method is verified under different test conditions.

KW - Improved particle swarm optimization (IPSO)

KW - lithium-ion battery

KW - self-adaptive super twisting sliding mode observer (SASTSMO)

KW - state of charge (SOC)

UR - https://www.scopus.com/pages/publications/85179505132

U2 - 10.1109/IECON51785.2023.10311754

DO - 10.1109/IECON51785.2023.10311754

M3 - 会议稿件

AN - SCOPUS:85179505132

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

Y2 - 16 October 2023 through 19 October 2023

ER -

Robust State of Charge Estimation for Battery with Self-Adaptive Super Twisting Sliding Mode Observer

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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