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Battery management systems may rely on mathematical models to provide higherperformance than standard charging protocols. Electrochemical models allow usto capture the phenomena occurring inside a lithium-ion cell and therefore,could be the best model choice. However, to be of practical value, they requirereliable model parameters. Uncertainty quantification and optimal experimentaldesign concepts are essential tools for identifying systems and estimatingparameters precisely. Approximation errors in uncertainty quantification resultin sub-optimal experimental designs and consequently, less-informative data,and higher parameter unreliability. In this work, we propose a highly efficientdesign of experiment method based on global parameter sensitivities. This novelconcept is applied to the single-particle model with electrolyte and thermaldynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. Theproposed method avoids the simplifying assumption of output-parameterlinearization (i.e., local parameter sensitivities) used in conventional Fisherinformation matrix-based experimental design strategies. Thus, the optimizedcurrent input profile results in experimental data of higher informationcontent and in turn, in more precise parameter estimates.

Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context