Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM |
| Editors | Chetan S. Kulkarni, Indranil Roychoudhury |
| Number of pages | 8 |
| Edition | 1 |
| ISBN (electronic) | 9781936263059 |
| Publication status | Published - 23 Oct 2023 |
| Event | 15th Annual Conference of the Prognostics and Health Management Society, PHM 2023 - Salt Lake City, United States Duration: 28 Oct 2023 → 2 Nov 2023 |
Publication series
| Name | Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM |
|---|---|
| Number | 1 |
| Volume | 15 |
| 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.
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Engineering(all)
- Electrical and Electronic Engineering
- Health Professions(all)
- Health Information Management
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM. ed. / Chetan S. Kulkarni; Indranil Roychoudhury. 1. ed. 2023. (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 15, No. 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries
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
PY - 2023/10/23
Y1 - 2023/10/23
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85178348288&partnerID=8YFLogxK
U2 - 10.36001/phmconf.2023.v15i1.3506
DO - 10.36001/phmconf.2023.v15i1.3506
M3 - Conference contribution
AN - SCOPUS:85178348288
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Kulkarni, Chetan S.
A2 - Roychoudhury, Indranil
T2 - 15th Annual Conference of the Prognostics and Health Management Society, PHM 2023
Y2 - 28 October 2023 through 2 November 2023
ER -