A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

  • Francesco Cancelliere
  • Sylvain Girard
  • Jean Marc Bourinet
  • Matteo Broggi

Externe Organisationen

  • Phimeca Engineering S.A.
  • SIGMA Clermont
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des SammelwerksProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Herausgeber/-innenChetan S. Kulkarni, Indranil Roychoudhury
Seitenumfang8
Auflage1
ISBN (elektronisch)9781936263059
PublikationsstatusVeröffentlicht - 23 Okt. 2023
Veranstaltung15th Annual Conference of the Prognostics and Health Management Society, PHM 2023 - Salt Lake City, USA / Vereinigte Staaten
Dauer: 28 Okt. 20232 Nov. 2023

Publikationsreihe

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Nummer1
Band15
ISSN (Print)2325-0178

Abstract

The lithium-ion batteries (LIB) industry is rapidly growing and is expected to continue expanding exponentially in the next decade. LIBs are already widely used in everyday life, and their demand is expected to increase further, particularly in the automotive sector. The European Union has introduced a new law to ban internal combustion engines from 2035, pushing for the adoption of electric vehicles and increasing the need for more efficient and reliable energy storage solutions such as LIBs. As a result, the establishment of Gigafactories in Europe and the United States is accelerating to meet the growing demand and partially reduce dependencies on China, which is currently the main producer of LIBs. To fully realize the potential of LIBs and ensure their safe and sustainable use, it is crucial to optimize their useful life and develop reliable and robust methodologies for estimating their state of health and predicting their remaining useful life. This requires a comprehensive understanding of LIB behaviour and the development of effective prognostic and health management approaches that can accurately predict battery degradation, plan for maintenance and replacements, and improve battery performance and lifespan. This work, funded by the GREYDIENT project, a European consortium aiming to advance the state of the art in the grey-box approach, combines physical modeling (white box) and machine learning (black box) techniques to demonstrate the grey-box effectiveness in the prognostic and health management. The grey-box approach here proposed consists of a combination of a physical battery model whose degradation parameters are estimated online at every cycle by a multilayer perceptron particle filter (MLP-PF). An electrochemical degradation model of a Lithium-Ion battery cell has been derived by use of Modelica. The model simulates the output voltage of the cell, while the degradation over time is simulated through the variation of 3 parameters: qMax (maximum number of lithium-ions available), R0 (internal resistance) and D (diffusion coefficient). To validate the model we resort to the well-known NASA Battery dataset, which has also been used to infer the optimal values of the three hidden degradation parameters at every cycle, to obtain their run-to-failure history. Then, the physical model is combined with the MLP-PF: an MLP artificial neural network is firstly trained on the run-to-failure degradation processes of the model parameters, allowing the propagation of the parameters in the future and the corresponding estimation of the battery remaining useful life (RUL). The MLP is then updated online by a particle filter every time a new measurement is available from the battery management system (BMS), providing flexibility to this method, needed for the electrochemical nature of the batteries, and allowing the propagation of uncertainties.

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A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries. / Cancelliere, Francesco; Girard, Sylvain; Bourinet, Jean Marc et al.
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. Hrsg. / Chetan S. Kulkarni; Indranil Roychoudhury. 1. Aufl. 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Band 15, Nr. 1).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Cancelliere, F, Girard, S, Bourinet, JM & Broggi, M 2023, A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries. in CS Kulkarni & I Roychoudhury (Hrsg.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. 1 Aufl., Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, Nr. 1, Bd. 15, 15th Annual Conference of the Prognostics and Health Management Society, PHM 2023, Salt Lake City, USA / Vereinigte Staaten, 28 Okt. 2023. https://doi.org/10.36001/phmconf.2023.v15i1.3506
Cancelliere, F., Girard, S., Bourinet, J. M., & Broggi, M. (2023). A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries. In C. S. Kulkarni, & I. Roychoudhury (Hrsg.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (1 Aufl.). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Band 15, Nr. 1). https://doi.org/10.36001/phmconf.2023.v15i1.3506
Cancelliere F, Girard S, Bourinet JM, Broggi M. A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries. in Kulkarni CS, Roychoudhury I, Hrsg., Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. 1 Aufl. 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; 1). doi: 10.36001/phmconf.2023.v15i1.3506
Cancelliere, Francesco ; Girard, Sylvain ; Bourinet, Jean Marc et al. / A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. Hrsg. / Chetan S. Kulkarni ; Indranil Roychoudhury. 1. Aufl. 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; 1).
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AU - Cancelliere, Francesco

AU - Girard, Sylvain

AU - Bourinet, Jean Marc

AU - Broggi, Matteo

N1 - Funding Information: The project leading to this application has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 955393

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N2 - The lithium-ion batteries (LIB) industry is rapidly growing and is expected to continue expanding exponentially in the next decade. LIBs are already widely used in everyday life, and their demand is expected to increase further, particularly in the automotive sector. The European Union has introduced a new law to ban internal combustion engines from 2035, pushing for the adoption of electric vehicles and increasing the need for more efficient and reliable energy storage solutions such as LIBs. As a result, the establishment of Gigafactories in Europe and the United States is accelerating to meet the growing demand and partially reduce dependencies on China, which is currently the main producer of LIBs. To fully realize the potential of LIBs and ensure their safe and sustainable use, it is crucial to optimize their useful life and develop reliable and robust methodologies for estimating their state of health and predicting their remaining useful life. This requires a comprehensive understanding of LIB behaviour and the development of effective prognostic and health management approaches that can accurately predict battery degradation, plan for maintenance and replacements, and improve battery performance and lifespan. This work, funded by the GREYDIENT project, a European consortium aiming to advance the state of the art in the grey-box approach, combines physical modeling (white box) and machine learning (black box) techniques to demonstrate the grey-box effectiveness in the prognostic and health management. The grey-box approach here proposed consists of a combination of a physical battery model whose degradation parameters are estimated online at every cycle by a multilayer perceptron particle filter (MLP-PF). An electrochemical degradation model of a Lithium-Ion battery cell has been derived by use of Modelica. The model simulates the output voltage of the cell, while the degradation over time is simulated through the variation of 3 parameters: qMax (maximum number of lithium-ions available), R0 (internal resistance) and D (diffusion coefficient). To validate the model we resort to the well-known NASA Battery dataset, which has also been used to infer the optimal values of the three hidden degradation parameters at every cycle, to obtain their run-to-failure history. Then, the physical model is combined with the MLP-PF: an MLP artificial neural network is firstly trained on the run-to-failure degradation processes of the model parameters, allowing the propagation of the parameters in the future and the corresponding estimation of the battery remaining useful life (RUL). The MLP is then updated online by a particle filter every time a new measurement is available from the battery management system (BMS), providing flexibility to this method, needed for the electrochemical nature of the batteries, and allowing the propagation of uncertainties.

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