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A surrogate-assiste...
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Alipour, MohammadUppsala universitet,Strukturkemi
(författare)
A surrogate-assisted uncertainty quantification and sensitivity analysis on a coupled electrochemical–thermal battery aging model
- Artikel/kapitelEngelska2023
Förlag, utgivningsår, omfång ...
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Elsevier BV,2023
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printrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:kth-331477
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-331477URI
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https://doi.org/10.1016/j.jpowsour.2023.233273DOI
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-507910URI
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Språk:engelska
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Sammanfattning på:engelska
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:art swepub-publicationtype
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QC 20230711
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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.
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Biuppslag (personer, institutioner, konferenser, titlar ...)
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Yin, LitaoUppsala universitet,Strukturkemi(Swepub:uu)lityi772
(författare)
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Tavallaey, Shiva SanderKTH,Teknisk mekanik,ABB AB Corporate Research, Forskargrand 7, SE-721 78 Västerås, Sweden, Forskargränd 7,ABB AB Corp Res, Forskargrand 7, SE-72178 Västerås, Sweden.;KTH, Dept Mech, Sch Sci, SE-10044 Stockholm, Sweden.(Swepub:kth)u1pyuj2p
(författare)
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Andersson, Anna MikaelaABB AB Corporate Research, Forskargrand 7, SE-721 78 Västerås, Sweden,ABB AB Corp Res, Forskargrand 7, SE-72178 Västerås, Sweden.
(författare)
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Brandell, Daniel,1975-Uppsala universitet,Strukturkemi(Swepub:uu)dbr10334
(författare)
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Uppsala universitetStrukturkemi
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Journal of Power Sources: Elsevier BV5790378-77531873-2755
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