A Computational Framework for Identifiability and Ill-Conditioning Analysis of Lithium-Ion Battery Models
The lack of informative experimental data and the complexity of first-principles battery models make the recovery of kinetic, transport, and thermodynamic parameters complicated. We present a computational framework that combines sensitivity, singular value, and Monte Carlo analysis to explore how different sources of experimental data affect parameter structural ill conditioning and identifiability. Our study is conducted on a modified version of the Doyle-Fuller-Newman model. We demonstrate that the use of voltage discharge curves only enables the identification of a small parameter subset, regardless of the number of experiments considered. Furthermore, we show that the inclusion of a single electrolyte concentration measurement significantly aids identifiability and mitigates ill-conditioning.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1391921
- Journal Information:
- Industrial and Engineering Chemistry Research, Vol. 55, Issue 11; ISSN 0888-5885
- Publisher:
- American Chemical Society (ACS)
- Country of Publication:
- United States
- Language:
- English
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