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A surrogate-assisted uncertainty quantification and sensitivity analysis on a coupled electrochemical–thermal battery aging model
Department of Chemistry - Angstrom Laboratory, Uppsala University, 751 21 Uppsala, Sweden.ORCID iD: 0000-0002-7862-2729
Department of Chemistry - Angstrom Laboratory, Uppsala University, 751 21 Uppsala, Sweden.
KTH, School of Engineering Sciences (SCI), Engineering Mechanics. ABB AB Corporate Research, Forskargrand 7, SE-721 78 Västerås, Sweden, Forskargränd 7.ORCID iD: 0000-0001-6551-9784
ABB AB Corporate Research, Forskargrand 7, SE-721 78 Västerås, Sweden.
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2023 (English)In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 579, article id 233273Article in journal (Refereed) Published
Abstract [en]

High-fidelity physics-based models are required to comprehend battery behavior at various operating conditions. This paper proposes an uncertainty quantification analysis on a coupled electrochemical–thermal aging model to improve the reliability of a battery model, while also investigating the impact of parametric model uncertainties on battery voltage, temperature, and aging. The coupled model's high computing cost, however, is a significant barrier to perform uncertainty quantification (UQ) and sensitivity analysis (SA). To address this problem, a surrogate model – i.e, by simulating the outcome of a quantity of interest that cannot be easily computed or measured – based on the Gaussian process regression (GPR) theory and principle component analysis (PCA) is built, using a small collection of finite element simulation results as synthetic training data. In total, 43 variable electrochemical–thermal parameters as well as 13 variable aging parameters are studied and estimated. Moreover, the trained surrogate model is also used in the parameterization of the electrochemical and thermal models. The results show that the uncertainties in the input parameters significantly affect the estimations of battery voltage, temperature, and aging. Based on this sensitivity analysis, the most influential parameters affecting the above mentioned battery outputs are reported. This approach is thereby helpful for developing robust and reliable high-fidelity battery aging models with potential applications in digital twins as well as for synthetic data generation.

Place, publisher, year, edition, pages
Elsevier BV , 2023. Vol. 579, article id 233273
Keywords [en]
Coupled electrochemical–thermal model, Li-ion battery aging, Parameter optimization, Sensitivity analysis, Surrogate model, Uncertainty quantification
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-331477DOI: 10.1016/j.jpowsour.2023.233273ISI: 001018004000001Scopus ID: 2-s2.0-85161265868OAI: oai:DiVA.org:kth-331477DiVA, id: diva2:1781804
Note

QC 20230711

Available from: 2023-07-11 Created: 2023-07-11 Last updated: 2023-09-05Bibliographically approved

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Tavallaey, Shiva Sander

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