State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

Farshid Naseri; Erik Schaltz; Daniel Ion Stroe; Alejandro Gismero; Ebrahim Farjah; Sepehr Karimi

doi:10.1109/PEDSTC52094.2021.9405847

State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

Farshid Naseri, Erik Schaltz, Daniel Ion Stroe, Alejandro Gismero, Ebrahim Farjah, Sepehr Karimi

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

4 Citations (Scopus)

19 Downloads (Pure)

Abstract

Lithium-ion (Li) battery based on nickel-manganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.

Original language	English
Title of host publication	2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021
Number of pages	5
Publisher	IEEE Signal Processing Society
Publication date	2 Feb 2021
Article number	9405847
ISBN (Print)	978-1-6654-4772-0
ISBN (Electronic)	978-1-6654-0366-5
DOIs	https://doi.org/10.1109/PEDSTC52094.2021.9405847
Publication status	Published - 2 Feb 2021
Event	12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021 - Tabriz, Iran, Islamic Republic of Duration: 2 Feb 2021 → 4 Feb 2021

Conference

Conference	12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021
Country/Territory	Iran, Islamic Republic of
City	Tabriz
Period	02/02/2021 → 04/02/2021

Series	2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work has been supported in part by Iran's National Elite's Foundation, in part by Electric Mobility Europe Call 2016 (ERA-NET COFUND) and Innovation Fund Denmark grant number 7064-00011B as part of the transnational eVolution2Grid (V2G) project.

Publisher Copyright:
© 2021 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

Battery Management System (BMS)
State Estimation
System Identification

Access to Document

10.1109/PEDSTC52094.2021.9405847

pedstc12-11082021009399Accepted author manuscript, 848 KB

Cite this

Naseri, F., Schaltz, E., Stroe, D. I., Gismero, A., Farjah, E., & Karimi, S. (2021). State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter. In 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021 [9405847] IEEE Signal Processing Society. 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021 https://doi.org/10.1109/PEDSTC52094.2021.9405847

Naseri, Farshid ; Schaltz, Erik ; Stroe, Daniel Ion et al. / State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter. 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021. IEEE Signal Processing Society, 2021. (2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021).

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abstract = "Lithium-ion (Li) battery based on nickel-manganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.",

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author = "Farshid Naseri and Erik Schaltz and Stroe, {Daniel Ion} and Alejandro Gismero and Ebrahim Farjah and Sepehr Karimi",

note = "Funding Information: ACKNOWLEDGMENT This work has been supported in part by Iran's National Elite's Foundation, in part by Electric Mobility Europe Call 2016 (ERA-NET COFUND) and Innovation Fund Denmark grant number 7064-00011B as part of the transnational eVolution2Grid (V2G) project. Publisher Copyright: {\textcopyright} 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021 ; Conference date: 02-02-2021 Through 04-02-2021",

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Naseri, F , Schaltz, E , Stroe, DI , Gismero, A, Farjah, E & Karimi, S 2021, State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter. in 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021., 9405847, IEEE Signal Processing Society, 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021, 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021, Tabriz, Iran, Islamic Republic of, 02/02/2021. https://doi.org/10.1109/PEDSTC52094.2021.9405847

State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter. / Naseri, Farshid ; Schaltz, Erik ; Stroe, Daniel Ion et al.

2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021. IEEE Signal Processing Society, 2021. 9405847 (2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021).

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

TY - GEN

T1 - State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

AU - Naseri, Farshid

AU - Schaltz, Erik

AU - Stroe, Daniel Ion

AU - Gismero, Alejandro

AU - Farjah, Ebrahim

AU - Karimi, Sepehr

N1 - Funding Information: ACKNOWLEDGMENT This work has been supported in part by Iran's National Elite's Foundation, in part by Electric Mobility Europe Call 2016 (ERA-NET COFUND) and Innovation Fund Denmark grant number 7064-00011B as part of the transnational eVolution2Grid (V2G) project. Publisher Copyright: © 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2/2

Y1 - 2021/2/2

N2 - Lithium-ion (Li) battery based on nickel-manganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.

AB - Lithium-ion (Li) battery based on nickel-manganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.

KW - Battery Management System (BMS)

KW - State Estimation

KW - System Identification

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U2 - 10.1109/PEDSTC52094.2021.9405847

DO - 10.1109/PEDSTC52094.2021.9405847

M3 - Article in proceeding

AN - SCOPUS:85105277481

SN - 978-1-6654-4772-0

T3 - 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021

BT - 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021

PB - IEEE Signal Processing Society

T2 - 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021

Y2 - 2 February 2021 through 4 February 2021

ER -

Naseri F , Schaltz E , Stroe DI , Gismero A, Farjah E, Karimi S. State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter. In 2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021. IEEE Signal Processing Society. 2021. 9405847. (2021 12th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2021). doi: 10.1109/PEDSTC52094.2021.9405847

State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

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Bibliographical note

Keywords

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