State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network

Sihan Zhang; Md Sazzad Hosen; Theodoros Kalogiannis; Joeri Van Mierlo; Maitane Berecibar

doi:10.3390/wevj12030156

State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network

Sihan Zhang, Md Sazzad Hosen, Theodoros Kalogiannis, Joeri Van Mierlo, Maitane Berecibar

Onderzoeksoutput: Article › peer review

16 Citaten (Scopus)

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Samenvatting

The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is presented. First, electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operational conditions and selected as key characteristic parameters for the model. Then, the model based on a backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimation of SoH with a mean-squared-error (MSE) of 0.002.

Originele taal-2	English
Artikelnummer	156
Aantal pagina's	14
Tijdschrift	World Electric Vehicle Journal
Volume	12
Nummer van het tijdschrift	3
DOI's	https://doi.org/10.3390/wevj12030156
Status	Published - 15 sep 2021

Bibliografische nota

Funding Information:
Funding: The authors would like to thank the Horizon2020 project GHOST (grant number 770019) for providing the fund and battery cells that are studied in this work.

Funding Information:
Acknowledgments: This research was developed under the framework of the GHOST project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 770019.

Funding Information:
The authors would like to thank the Horizon 2020 project GHOST (grant number 770019) for providing the fund and battery cells that are studied in this work.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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

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10.3390/wevj12030156Licentie: CC BY

wevj-12-00156Final published version, 611 KBLicentie: CC BY

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State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network
Kalogiannis, T. (Creator), Zenodo, 2024
DOI: 10.5281/zenodo.10886048
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Citeer dit

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abstract = "The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is presented. First, electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operational conditions and selected as key characteristic parameters for the model. Then, the model based on a backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimation of SoH with a mean-squared-error (MSE) of 0.002.",

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note = "Funding Information: Funding: The authors would like to thank the Horizon2020 project GHOST (grant number 770019) for providing the fund and battery cells that are studied in this work. Funding Information: Acknowledgments: This research was developed under the framework of the GHOST project that has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation program under grant agreement No 770019. Funding Information: The authors would like to thank the Horizon 2020 project GHOST (grant number 770019) for providing the fund and battery cells that are studied in this work. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

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N2 - The global electric vehicle (EV) is expanding enormously, foreseeing a 17.4% increase in compound annual growth rate (CAGR) by the end of 2027. The lithium-ion battery is considered as the most widely used battery technology in EV. The accurate and reliable diagnostic and prognostic of battery state guarantees the safe operation of EV and is crucial for durable electric vehicles. Research focusing on lithium-ion battery life degradation has grown more important in recent years. In this study, a model built for state of health (SoH) estimation for the LTO anode-based lithium-ion battery is presented. First, electrochemical impedance spectroscopy (EIS) is used to study the deterioration in battery performance, measurements such as charge transfer resistance and ohmic resistance are analyzed for different operational conditions and selected as key characteristic parameters for the model. Then, the model based on a backpropagation neural network (BPNN) along with the characteristic parameters is trained and validated with a real-life driving profile. The model shows a relatively accurate estimation of SoH with a mean-squared-error (MSE) of 0.002.

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State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network

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State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network

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