A Computational Framework for Identifiability and Ill-Conditioning Analysis of Lithium-Ion Battery Models

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• 作者：Diana C. López C ; Günter Wozny ; Antonio Flores-Tlacuahuac ; Ruben Vasquez-Medrano ; Victor M. Zavala
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2016
• 出版时间：March 23, 2016
• 年：2016
• 卷：55
• 期：11
• 页码：3026-3042
• 全文大小：890K
• ISSN：1520-5045

文摘

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.