Battery management systems may rely on mathematical models to provide higher
performance than standard charging protocols. Electrochemical models allow us
to capture the phenomena occurring inside a lithium-ion cell and therefore,
could be the best model choice. However, to be of practical value, they require
reliable model parameters. Uncertainty quantification and optimal experimental
design concepts are essential tools for identifying systems and estimating
parameters precisely. Approximation errors in uncertainty quantification result
in sub-optimal experimental designs and consequently, less-informative data,
and higher parameter unreliability. In this work, we propose a highly efficient
design of experiment method based on global parameter sensitivities. This novel
concept is applied to the single-particle model with electrolyte and thermal
dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The
proposed method avoids the simplifying assumption of output-parameter
linearization (i.e., local parameter sensitivities) used in conventional Fisher
information matrix-based experimental design strategies. Thus, the optimized
current input profile results in experimental data of higher information
content and in turn, in more precise parameter estimates.Comment: Accepted for 21st IFAC World Congres