HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort

Camiel J.J. Beckers; Feye S.J. Hoekstra; Frank P.T. Willems

doi:10.1109/VPPC63154.2024.10755448

HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort

Camiel J.J. Beckers, Feye S.J. Hoekstra, Frank P.T. Willems

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

Samenvatting

Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.

Originele taal-2	Engels
Titel	2024 IEEE Vehicle Power and Propulsion Conference, VPPC
Uitgeverij	Institute of Electrical and Electronics Engineers
Aantal pagina's	6
ISBN van elektronische versie	979-8-3315-4160-6
DOI's	https://doi.org/10.1109/VPPC63154.2024.10755448
Status	Gepubliceerd - 20 nov. 2024
Evenement	2024 IEEE Vehicle Power and Propulsion - Washington DC, Verenigde Staten van Amerika Duur: 7 okt. 2024 → 10 okt. 2024 https://events.vtsociety.org/vppc2024/

Congres

Congres	2024 IEEE Vehicle Power and Propulsion
Verkorte titel	IEEE VPPC 2024
Land/Regio	Verenigde Staten van Amerika
Stad	Washington DC
Periode	7/10/24 → 10/10/24
Internet adres	https://events.vtsociety.org/vppc2024/

Financiering

This work has received financial support from the Dutch Ministry of Economic Affairs and Climate, under the grant 'R&D Mobility Sectors' and as part TNO's Early Research Program in the scope of the project 'AUTO ADAPT'.

Financiers	Financiernummer
Ministerie van Economische Zaken en Klimaat

Duurzame ontwikkelingsdoelstellingen van de VN

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10.1109/VPPC63154.2024.10755448

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title = "HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort",

abstract = "Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.",

keywords = "Battery Management System (BMS), Forgetting-Factor Recursive Least-Squares (FFRLS), Hardware-in-the-Loop (HiL), Parameter Estimation, State-of-Health (SoH)",

author = "Beckers, {Camiel J.J.} and Hoekstra, {Feye S.J.} and Willems, {Frank P.T.}",

year = "2024",

month = nov,

day = "20",

doi = "10.1109/VPPC63154.2024.10755448",

language = "English",

booktitle = "2024 IEEE Vehicle Power and Propulsion Conference, VPPC",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2024 IEEE Vehicle Power and Propulsion , IEEE VPPC 2024 ; Conference date: 07-10-2024 Through 10-10-2024",

url = "https://events.vtsociety.org/vppc2024/",

}

Beckers, CJJ, Hoekstra, FSJ & Willems, FPT 2024, HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort. in 2024 IEEE Vehicle Power and Propulsion Conference, VPPC., 10755448, Institute of Electrical and Electronics Engineers, 2024 IEEE Vehicle Power and Propulsion , Washington DC, Washington, Verenigde Staten van Amerika, 7/10/24. https://doi.org/10.1109/VPPC63154.2024.10755448

HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort. / Beckers, Camiel J.J.; Hoekstra, Feye S.J.; Willems, Frank P.T.
2024 IEEE Vehicle Power and Propulsion Conference, VPPC. Institute of Electrical and Electronics Engineers, 2024. 10755448.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - Hoekstra, Feye S.J.

AU - Willems, Frank P.T.

PY - 2024/11/20

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N2 - Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.

AB - Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.

KW - Battery Management System (BMS)

KW - Forgetting-Factor Recursive Least-Squares (FFRLS)

KW - Hardware-in-the-Loop (HiL)

KW - Parameter Estimation

KW - State-of-Health (SoH)

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PB - Institute of Electrical and Electronics Engineers

T2 - 2024 IEEE Vehicle Power and Propulsion

Y2 - 7 October 2024 through 10 October 2024

ER -

HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort

Samenvatting

Congres

Financiering

Duurzame ontwikkelingsdoelstellingen van de VN

Toegang tot document

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