Li-ion Battery Digital Twin Based on Online Impedance Estimation

Abhijit Kulkarni; Hoda Sorouri; Yusheng Zheng; Xin Sui; Arman Oshnoei; Nicolai Andre Weinreich; Remus Teodorescu

doi:10.1109/CPE-POWERENG58103.2023.10227419

Li-ion Battery Digital Twin Based on Online Impedance Estimation

Abhijit Kulkarni^*, Hoda Sorouri, Yusheng Zheng, Xin Sui, Arman Oshnoei, Nicolai Andre Weinreich, Remus Teodorescu

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

5 Citations (Scopus)

21 Downloads (Pure)

Abstract

The battery digital twin (BDT) is a modern tool that will be used in future intelligent battery management systems (BMS) for Li-Ion batteries (LIB) due to the transition of current technology toward Smart Battery (SB) with information and power processing capability at cell level. The BDT can predict the voltage output based on an impedance model at a given temperature and aging condition and this information can be used for advanced state estimation including sensorless state of temperature (SoT), state of health (SoH) and health management. This paper proposes an online impedance estimation method suitable for the smart battery system which includes a bypass device that can be switched to excite the battery impedance with different frequencies and minimum impact on the load. The performance of the proposed impedance model used in the BDT is compared experimentally in terms of accuracy of the voltage response to dynamic current profiles.

Original language	English
Title of host publication	CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering
Publisher	IEEE
Publication date	2023
Article number	10227419
ISBN (Electronic)	9798350300048
DOIs	https://doi.org/10.1109/CPE-POWERENG58103.2023.10227419
Publication status	Published - 2023
Event	17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023 - Tallinn, Estonia Duration: 14 Jun 2023 → 16 Jun 2023

Conference

Conference	17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023
Country/Territory	Estonia
City	Tallinn
Period	14/06/2023 → 16/06/2023

Series	CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

digital twin
health management
Smart battery

Access to Document

10.1109/CPE-POWERENG58103.2023.10227419

CPE_final_v0Accepted author manuscript, 927 KBLicence: CC BY 4.0

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

Kulkarni, Abhijit ; Sorouri, Hoda ; Zheng, Yusheng et al. / Li-ion Battery Digital Twin Based on Online Impedance Estimation. CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering. IEEE, 2023. (CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering).

@inproceedings{4e834a213b94426280a11557cc39065c,

title = "Li-ion Battery Digital Twin Based on Online Impedance Estimation",

abstract = "The battery digital twin (BDT) is a modern tool that will be used in future intelligent battery management systems (BMS) for Li-Ion batteries (LIB) due to the transition of current technology toward Smart Battery (SB) with information and power processing capability at cell level. The BDT can predict the voltage output based on an impedance model at a given temperature and aging condition and this information can be used for advanced state estimation including sensorless state of temperature (SoT), state of health (SoH) and health management. This paper proposes an online impedance estimation method suitable for the smart battery system which includes a bypass device that can be switched to excite the battery impedance with different frequencies and minimum impact on the load. The performance of the proposed impedance model used in the BDT is compared experimentally in terms of accuracy of the voltage response to dynamic current profiles.",

keywords = "digital twin, health management, Smart battery",

author = "Abhijit Kulkarni and Hoda Sorouri and Yusheng Zheng and Xin Sui and Arman Oshnoei and Weinreich, {Nicolai Andre} and Remus Teodorescu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023 ; Conference date: 14-06-2023 Through 16-06-2023",

year = "2023",

doi = "10.1109/CPE-POWERENG58103.2023.10227419",

language = "English",

series = "CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering",

booktitle = "CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering",

publisher = "IEEE",

address = "United States",

}

Kulkarni, A, Sorouri, H , Zheng, Y , Sui, X , Oshnoei, A , Weinreich, NA & Teodorescu, R 2023, Li-ion Battery Digital Twin Based on Online Impedance Estimation. in CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering., 10227419, IEEE, CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023, Tallinn, Estonia, 14/06/2023. https://doi.org/10.1109/CPE-POWERENG58103.2023.10227419

Li-ion Battery Digital Twin Based on Online Impedance Estimation. / Kulkarni, Abhijit; Sorouri, Hoda ; Zheng, Yusheng et al.
CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering. IEEE, 2023. 10227419 (CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Li-ion Battery Digital Twin Based on Online Impedance Estimation

AU - Kulkarni, Abhijit

AU - Sorouri, Hoda

AU - Zheng, Yusheng

AU - Sui, Xin

AU - Oshnoei, Arman

AU - Weinreich, Nicolai Andre

AU - Teodorescu, Remus

PY - 2023

Y1 - 2023

N2 - The battery digital twin (BDT) is a modern tool that will be used in future intelligent battery management systems (BMS) for Li-Ion batteries (LIB) due to the transition of current technology toward Smart Battery (SB) with information and power processing capability at cell level. The BDT can predict the voltage output based on an impedance model at a given temperature and aging condition and this information can be used for advanced state estimation including sensorless state of temperature (SoT), state of health (SoH) and health management. This paper proposes an online impedance estimation method suitable for the smart battery system which includes a bypass device that can be switched to excite the battery impedance with different frequencies and minimum impact on the load. The performance of the proposed impedance model used in the BDT is compared experimentally in terms of accuracy of the voltage response to dynamic current profiles.

AB - The battery digital twin (BDT) is a modern tool that will be used in future intelligent battery management systems (BMS) for Li-Ion batteries (LIB) due to the transition of current technology toward Smart Battery (SB) with information and power processing capability at cell level. The BDT can predict the voltage output based on an impedance model at a given temperature and aging condition and this information can be used for advanced state estimation including sensorless state of temperature (SoT), state of health (SoH) and health management. This paper proposes an online impedance estimation method suitable for the smart battery system which includes a bypass device that can be switched to excite the battery impedance with different frequencies and minimum impact on the load. The performance of the proposed impedance model used in the BDT is compared experimentally in terms of accuracy of the voltage response to dynamic current profiles.

KW - digital twin

KW - health management

KW - Smart battery

UR - http://www.scopus.com/inward/record.url?scp=85171533320&partnerID=8YFLogxK

U2 - 10.1109/CPE-POWERENG58103.2023.10227419

DO - 10.1109/CPE-POWERENG58103.2023.10227419

M3 - Article in proceeding

AN - SCOPUS:85171533320

T3 - CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering

BT - CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering

PB - IEEE

T2 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023

Y2 - 14 June 2023 through 16 June 2023

ER -

Kulkarni A, Sorouri H , Zheng Y , Sui X , Oshnoei A , Weinreich NA et al. Li-ion Battery Digital Twin Based on Online Impedance Estimation. In CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering. IEEE. 2023. 10227419. (CPE-POWERENG 2023 - 17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering). doi: 10.1109/CPE-POWERENG58103.2023.10227419

Li-ion Battery Digital Twin Based on Online Impedance Estimation

Abstract

Conference

Bibliographical note

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

OPENSRUM: Optimal Power Conversion and Energy Storage System for Safe and Reliable Urban Air Mobility

Cite this

Li-ion Battery Digital Twin Based on Online Impedance Estimation

Abstract

Conference

Bibliographical note

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

OPENSRUM: Optimal Power Conversion and Energy Storage System for Safe and Reliable Urban Air Mobility

Cite this