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

Recovering kinetic, transport, and thermodynamic parameters is a key task in the development of battery models. This task is complicated because of the lack of informative experimental data and because of the complexity of the associated partial differential equation models. We present a computational framework that combines a variety of techniques to investigate the effects that different sources of experimental information on parameter identifiability and on structural illconditioning. We analyze the electrochemical isothermal Lithium-ion model developed and validated by Doyle et al. which consists of a lithiated-carbon anode (LixC6), a polymer electrolyte, and a lithium-manganese-oxide cathode (LiyMn2O4). We use our framework to guide the selection of experimental information. We demonstrate that the use of voltage discharge information enables the identification of a small parameter subset, regardless of the number of experiments considered. We also demonstrate that the use of electrolyte concentration information significantly aids identifiability and mitigates ill-conditioning.