Research on Battery State Estimation and Prediction Model Construction

Yan Gao; Xiaolei Shi; Fang Wang; Shiqiang Liu; Tianyi Ma; Pengfei Yan; Ce Han

doi:10.1007/978-3-031-69483-7_34

Research on Battery State Estimation and Prediction Model Construction

Yan Gao^*, Xiaolei Shi, Fang Wang, Shiqiang Liu, Tianyi Ma, Pengfei Yan, Ce Han

^*Corresponding author for this work

Ltd.

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

Abstract

The charging and discharging process of lithium-ion battery is the process of mutual conversion of electrical and chemical energy, and its performance will gradually decline during its use. However, in practical applications, it is difficult to accurately estimate and predict the state of lithium-ion batteries. In this study, the internal parameter evolution of the battery during storage is investigated by establishing a quasi-steady-state model of the lithium-ion battery, and a decay model is established for predicting the evolution of the battery. Using the established model, the correspondence between the external characteristics and the internal state of lithium-ion batteries containing composite electrodes is investigated, and the accurate estimation of the state of the composite electrodes is realized based on the steady-state voltage of the composite electrodes and the characteristics of their differential curves. The simulation results of the model are in high agreement with the experimental results, indicating that the established model can accurately describe the storage decay process of the battery.

Original language	English
Title of host publication	Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP
Editors	Tongtong Liu, Fan Zhang, Shiqing Huang, Jingjing Wang, Fengshou Gu
Publisher	Springer Science and Business Media B.V.
Pages	369-384
Number of pages	16
ISBN (Print)	9783031694820
DOIs	https://doi.org/10.1007/978-3-031-69483-7_34
Publication status	Published - 2024
Externally published	Yes
Event	TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024 - Qingdao, China Duration: 8 May 2024 → 11 May 2024

Publication series

Name	Mechanisms and Machine Science
Volume	169 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024
Country/Territory	China
City	Qingdao
Period	8/05/24 → 11/05/24

Keywords

Battery Decay Model
Life Prediction
Lithium-ion Battery
State Estimation
Whole Life Cycle

UN SDGs

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

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10.1007/978-3-031-69483-7_34

Cite this

Gao, Y., Shi, X., Wang, F., Liu, S., Ma, T., Yan, P., & Han, C. (2024). Research on Battery State Estimation and Prediction Model Construction. In T. Liu, F. Zhang, S. Huang, J. Wang, & F. Gu (Eds.), Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP (pp. 369-384). (Mechanisms and Machine Science; Vol. 169 MMS). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-031-69483-7_34

Gao, Yan ; Shi, Xiaolei ; Wang, Fang et al. / Research on Battery State Estimation and Prediction Model Construction. Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP. editor / Tongtong Liu ; Fan Zhang ; Shiqing Huang ; Jingjing Wang ; Fengshou Gu. Springer Science and Business Media B.V., 2024. pp. 369-384 (Mechanisms and Machine Science).

@inproceedings{1bad99845b464a24827b67875162bdf8,

title = "Research on Battery State Estimation and Prediction Model Construction",

abstract = "The charging and discharging process of lithium-ion battery is the process of mutual conversion of electrical and chemical energy, and its performance will gradually decline during its use. However, in practical applications, it is difficult to accurately estimate and predict the state of lithium-ion batteries. In this study, the internal parameter evolution of the battery during storage is investigated by establishing a quasi-steady-state model of the lithium-ion battery, and a decay model is established for predicting the evolution of the battery. Using the established model, the correspondence between the external characteristics and the internal state of lithium-ion batteries containing composite electrodes is investigated, and the accurate estimation of the state of the composite electrodes is realized based on the steady-state voltage of the composite electrodes and the characteristics of their differential curves. The simulation results of the model are in high agreement with the experimental results, indicating that the established model can accurately describe the storage decay process of the battery.",

keywords = "Battery Decay Model, Life Prediction, Lithium-ion Battery, State Estimation, Whole Life Cycle",

author = "Yan Gao and Xiaolei Shi and Fang Wang and Shiqiang Liu and Tianyi Ma and Pengfei Yan and Ce Han",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024 ; Conference date: 08-05-2024 Through 11-05-2024",

year = "2024",

doi = "10.1007/978-3-031-69483-7_34",

language = "English",

isbn = "9783031694820",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

pages = "369--384",

editor = "Tongtong Liu and Fan Zhang and Shiqing Huang and Jingjing Wang and Fengshou Gu",

booktitle = "Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP",

address = "Germany",

}

Gao, Y, Shi, X, Wang, F, Liu, S, Ma, T, Yan, P & Han, C 2024, Research on Battery State Estimation and Prediction Model Construction. in T Liu, F Zhang, S Huang, J Wang & F Gu (eds), Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP. Mechanisms and Machine Science, vol. 169 MMS, Springer Science and Business Media B.V., pp. 369-384, TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024, Qingdao, China, 8/05/24. https://doi.org/10.1007/978-3-031-69483-7_34

Research on Battery State Estimation and Prediction Model Construction. / Gao, Yan; Shi, Xiaolei; Wang, Fang et al.
Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP. ed. / Tongtong Liu; Fan Zhang; Shiqing Huang; Jingjing Wang; Fengshou Gu. Springer Science and Business Media B.V., 2024. p. 369-384 (Mechanisms and Machine Science; Vol. 169 MMS).

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

TY - GEN

T1 - Research on Battery State Estimation and Prediction Model Construction

AU - Gao, Yan

AU - Shi, Xiaolei

AU - Wang, Fang

AU - Liu, Shiqiang

AU - Ma, Tianyi

AU - Yan, Pengfei

AU - Han, Ce

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - The charging and discharging process of lithium-ion battery is the process of mutual conversion of electrical and chemical energy, and its performance will gradually decline during its use. However, in practical applications, it is difficult to accurately estimate and predict the state of lithium-ion batteries. In this study, the internal parameter evolution of the battery during storage is investigated by establishing a quasi-steady-state model of the lithium-ion battery, and a decay model is established for predicting the evolution of the battery. Using the established model, the correspondence between the external characteristics and the internal state of lithium-ion batteries containing composite electrodes is investigated, and the accurate estimation of the state of the composite electrodes is realized based on the steady-state voltage of the composite electrodes and the characteristics of their differential curves. The simulation results of the model are in high agreement with the experimental results, indicating that the established model can accurately describe the storage decay process of the battery.

AB - The charging and discharging process of lithium-ion battery is the process of mutual conversion of electrical and chemical energy, and its performance will gradually decline during its use. However, in practical applications, it is difficult to accurately estimate and predict the state of lithium-ion batteries. In this study, the internal parameter evolution of the battery during storage is investigated by establishing a quasi-steady-state model of the lithium-ion battery, and a decay model is established for predicting the evolution of the battery. Using the established model, the correspondence between the external characteristics and the internal state of lithium-ion batteries containing composite electrodes is investigated, and the accurate estimation of the state of the composite electrodes is realized based on the steady-state voltage of the composite electrodes and the characteristics of their differential curves. The simulation results of the model are in high agreement with the experimental results, indicating that the established model can accurately describe the storage decay process of the battery.

KW - Battery Decay Model

KW - Life Prediction

KW - Lithium-ion Battery

KW - State Estimation

KW - Whole Life Cycle

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DO - 10.1007/978-3-031-69483-7_34

M3 - Conference contribution

AN - SCOPUS:85204402794

SN - 9783031694820

T3 - Mechanisms and Machine Science

SP - 369

EP - 384

BT - Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP

A2 - Liu, Tongtong

A2 - Zhang, Fan

A2 - Huang, Shiqing

A2 - Wang, Jingjing

A2 - Gu, Fengshou

PB - Springer Science and Business Media B.V.

T2 - TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

Y2 - 8 May 2024 through 11 May 2024

ER -

Gao Y, Shi X, Wang F, Liu S, Ma T, Yan P et al. Research on Battery State Estimation and Prediction Model Construction. In Liu T, Zhang F, Huang S, Wang J, Gu F, editors, Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP. Springer Science and Business Media B.V. 2024. p. 369-384. (Mechanisms and Machine Science). doi: 10.1007/978-3-031-69483-7_34

Research on Battery State Estimation and Prediction Model Construction

Abstract

Publication series

Conference

Keywords

UN SDGs

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