Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries
High-fidelity electrochemical-thermal models are essential for performance improvement, charge/discharge strategy optimization, and the safe operation of lithium-ion batteries. However, model performance significantly relies on the accuracy of parameters, whose measurement is limited by laboratory c...
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Elsevier
2025-06-01
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| Series: | Advances in Applied Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666792425000150 |
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| author | Jue Chen Sven Patrick Mattus Wenjiong Cao Dirk Uwe Sauer Weihan Li |
| author_facet | Jue Chen Sven Patrick Mattus Wenjiong Cao Dirk Uwe Sauer Weihan Li |
| author_sort | Jue Chen |
| collection | DOAJ |
| description | High-fidelity electrochemical-thermal models are essential for performance improvement, charge/discharge strategy optimization, and the safe operation of lithium-ion batteries. However, model performance significantly relies on the accuracy of parameters, whose measurement is limited by laboratory conditions. Non-invasive methods based on relatively accessible current, voltage, and temperature data combined with artificial intelligence are promising for rapid parameterization of battery models. However, the model’s complexity and the data’s poor quality increase the difficulty of applying the methodology. To design a reasonable identification framework and obtain reliable data, the identifiability of model parameters must be analyzed under different operating conditions. This paper develops an identifiability analysis framework to investigate the impact of model parameters on voltage and temperature outputs and the impact of key operating variables, i.e., current rate and ambient temperature. By adjusting operating conditions, the sensitivity of specific parameters can be improved by two orders of magnitude. The results are discussed in detail concerning the model modeling mechanism and the physical meaning of the parameters, with a focus on improving non-invasive parameterization in terms of experimental design and identification strategy. |
| format | Article |
| id | doaj-art-80ae73e9518343c7a024d44dd09e7b6c |
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| issn | 2666-7924 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Advances in Applied Energy |
| spelling | doaj-art-80ae73e9518343c7a024d44dd09e7b6c2025-08-20T02:35:47ZengElsevierAdvances in Applied Energy2666-79242025-06-011810022110.1016/j.adapen.2025.100221Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteriesJue Chen0Sven Patrick Mattus1Wenjiong Cao2Dirk Uwe Sauer3Weihan Li4Center for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, Templergraben 55, Aachen, 52056, Germany; Corresponding authors at: Center for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany.Center for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, Templergraben 55, Aachen, 52056, GermanyCenter for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, Templergraben 55, Aachen, 52056, GermanyCenter for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, Templergraben 55, Aachen, 52056, Germany; Helmholtz Institute Muenster (HI MS), IMD-4, Forschungszentrum Juelich, GermanyCenter for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany; Juelich Aachen Research Alliance, JARA-Energy, Templergraben 55, Aachen, 52056, Germany; Corresponding authors at: Center for Aging, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL), RWTH Aachen University, Campus-Boulevard 89, 52074 Aachen, Germany.High-fidelity electrochemical-thermal models are essential for performance improvement, charge/discharge strategy optimization, and the safe operation of lithium-ion batteries. However, model performance significantly relies on the accuracy of parameters, whose measurement is limited by laboratory conditions. Non-invasive methods based on relatively accessible current, voltage, and temperature data combined with artificial intelligence are promising for rapid parameterization of battery models. However, the model’s complexity and the data’s poor quality increase the difficulty of applying the methodology. To design a reasonable identification framework and obtain reliable data, the identifiability of model parameters must be analyzed under different operating conditions. This paper develops an identifiability analysis framework to investigate the impact of model parameters on voltage and temperature outputs and the impact of key operating variables, i.e., current rate and ambient temperature. By adjusting operating conditions, the sensitivity of specific parameters can be improved by two orders of magnitude. The results are discussed in detail concerning the model modeling mechanism and the physical meaning of the parameters, with a focus on improving non-invasive parameterization in terms of experimental design and identification strategy.http://www.sciencedirect.com/science/article/pii/S2666792425000150Lithium-ion batteryElectrochemical-thermal modelSensitivityNon-invasiveParameterization |
| spellingShingle | Jue Chen Sven Patrick Mattus Wenjiong Cao Dirk Uwe Sauer Weihan Li Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries Advances in Applied Energy Lithium-ion battery Electrochemical-thermal model Sensitivity Non-invasive Parameterization |
| title | Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries |
| title_full | Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries |
| title_fullStr | Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries |
| title_full_unstemmed | Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries |
| title_short | Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries |
| title_sort | global sensitivity analysis towards non invasive parameterization of the electrochemical thermal model for lithium ion batteries |
| topic | Lithium-ion battery Electrochemical-thermal model Sensitivity Non-invasive Parameterization |
| url | http://www.sciencedirect.com/science/article/pii/S2666792425000150 |
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