Optimal Parameter Estimation of CAPN Model for Li-ion Battery

Soumya Bharti; Vikash Kumar Saini; Anshul Kumar Yadav; Rajesh Kumar; Ameena S. Al-Sumaiti

doi:10.1109/IC2E357697.2023.10262727

Optimal Parameter Estimation of CAPN Model for Li-ion Battery

Soumya Bharti
, Vikash Kumar Saini
, Anshul Kumar Yadav
, Rajesh Kumar
, Ameena S. Al-Sumaiti

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

6 Citations (Scopus)

Abstract

Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.

Original language	English
Title of host publication	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350338003
DOIs	https://doi.org/10.1109/IC2E357697.2023.10262727
Publication status	Published - 2023
Externally published	Yes
Event	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 - Srinagar Garhwal, India Duration: 8 Jun 2023 → 9 Jun 2023

Publication series

Name	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

Conference

Conference	2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Country/Territory	India
City	Srinagar Garhwal
Period	8/06/23 → 9/06/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Battery degradation model
Li-ion battery
Optimization algorithm

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering
Control and Optimization
Instrumentation

Access to Document

10.1109/IC2E357697.2023.10262727

Cite this

Bharti, S., Saini, V. K., Kumar Yadav, A., Kumar, R., & Al-Sumaiti, A. S. (2023). Optimal Parameter Estimation of CAPN Model for Li-ion Battery. In 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IC2E357697.2023.10262727

Bharti, Soumya ; Saini, Vikash Kumar ; Kumar Yadav, Anshul et al. / Optimal Parameter Estimation of CAPN Model for Li-ion Battery. 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023).

@inproceedings{e4a5a21ffb3849eab1eaa2db090b5a04,

title = "Optimal Parameter Estimation of CAPN Model for Li-ion Battery",

abstract = "Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.",

keywords = "Battery degradation model, Li-ion battery, Optimization algorithm",

author = "Soumya Bharti and Saini, \{Vikash Kumar\} and \{Kumar Yadav\}, Anshul and Rajesh Kumar and Al-Sumaiti, \{Ameena S.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023 ; Conference date: 08-06-2023 Through 09-06-2023",

year = "2023",

doi = "10.1109/IC2E357697.2023.10262727",

language = "English",

series = "2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bharti, S, Saini, VK, Kumar Yadav, A, Kumar, R & Al-Sumaiti, AS 2023, Optimal Parameter Estimation of CAPN Model for Li-ion Battery. in 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023, Srinagar Garhwal, India, 8/06/23. https://doi.org/10.1109/IC2E357697.2023.10262727

Optimal Parameter Estimation of CAPN Model for Li-ion Battery. / Bharti, Soumya; Saini, Vikash Kumar; Kumar Yadav, Anshul et al.
2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023).

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

TY - GEN

T1 - Optimal Parameter Estimation of CAPN Model for Li-ion Battery

AU - Bharti, Soumya

AU - Saini, Vikash Kumar

AU - Kumar Yadav, Anshul

AU - Kumar, Rajesh

AU - Al-Sumaiti, Ameena S.

PY - 2023

Y1 - 2023

N2 - Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.

AB - Recent developments in battery technology and the reduction of battery costs have resulted in battery energy storage systems (BESS) becoming more economically viable for various power sectors and automobile applications. The degradation of BESS when used in conjunction with photovoltaics (PV) and electric vehicles (EVs) brings new challenges in both technical and economic analysis. Consequently, to ensure the reliability and safety of an energy storage system, modeling battery degradation is critical in order to maintain and replace batteries before potential issues arise. This paper presents a hybrid optimal parameter estimation technique for a combination-factor-based semi-empirical approach to evaluate the state of health (SOH) of Li-ion batteries. The parameters of the proposed model are determined using the SCA optimization algorithm, and the numerical results are confirmed by placing them side-by-side with other heuristic algorithms such as GWO, DE, HHO, and GA. The proposed model outperformed the traditional model with a SOH value of 0.9591 as compared to the previous SOH obtained 0.9467 by the traditional model.

KW - Battery degradation model

KW - Li-ion battery

KW - Optimization algorithm

UR - https://www.scopus.com/pages/publications/85174496984

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DO - 10.1109/IC2E357697.2023.10262727

M3 - Conference contribution

AN - SCOPUS:85174496984

T3 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

BT - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

Y2 - 8 June 2023 through 9 June 2023

ER -

Bharti S, Saini VK, Kumar Yadav A, Kumar R, Al-Sumaiti AS. Optimal Parameter Estimation of CAPN Model for Li-ion Battery. In 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023). doi: 10.1109/IC2E357697.2023.10262727

Optimal Parameter Estimation of CAPN Model for Li-ion Battery

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this