The Library
Probabilistic sensitivity analysis for multivariate model outputs with applications to Li-ion batteries
Tools
Tools
Tools
Triantafyllidis, Vasileios, Xing, W. W., Leung, Puiki and Shah, Akeel A. (2017) Probabilistic sensitivity analysis for multivariate model outputs with applications to Li-ion batteries. Journal of Physics: Conference Series, 1039 . 012020. doi:10.1088/1742-6596/1039/1/012020 ISSN 1742-6596.
|
PDF
WRAP-probabilistic-sensitivity-analysis-multivariate-Li-ion-batteries-Shah-2018.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution. Download (499Kb) | Preview |
|
![[img]](https://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-probabilistic-sensitivity-analysis-multivariate-model-batteries-Shah-2017.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (370Kb) |
Official URL: https://doi.org/10.1088/1742-6596/1039/1/012020
Abstract
Full battery models are highly complex, which limits their application to tasks such as optimization and uncertainty quantification. To lower the computational burden, sensitivity analysis (SA) can be used as a precursor to identify the most important parameters in the model, but SA itself relies on a high number of full model evaluations, which has motivated the use of emulators. For high-dimensional output problems, emulators are challenging to construct. In this paper we develop a probabilistic framework for SA of high-dimensional output models using a Gaussian process emulator based on dimensionality reduction. This allows us to perform SA under uncertainty for multi-ouput problems, providing error bounds for the emulator predictions of sensitivity measures. We show how this can be achieved using Monte Carlo sampling or possibly by using semi-analytical expressions with highly efficient sampling. Moreover, we can perform SA for multivariate outputs by ranking the sensitivity measures related to (uncorrelated) coefficients in a basis for the output space.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Lithium ion batteries -- Mathematical models | ||||||
| Journal or Publication Title: | Journal of Physics: Conference Series | ||||||
| Publisher: | Institute of Physics Publishing Ltd. | ||||||
| ISSN: | 1742-6596 | ||||||
| Official Date: | 15 November 2017 | ||||||
| Dates: |
|
||||||
| Volume: | 1039 | ||||||
| Article Number: | 012020 | ||||||
| DOI: | 10.1088/1742-6596/1039/1/012020 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 5 December 2017 | ||||||
| Date of first compliant Open Access: | 10 September 2018 | ||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
