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In this work, a stochastic, physics-based model for Lithium-ion batteries(LIBs) is presented in order to study the effects of parametric modeluncertainties on the cell capacity, voltage, and concentrations. To this end,the proposed uncertainty quantification (UQ) approach, based on sparsepolynomial chaos expansions, relies on a small number of battery simulations.Within this UQ framework, the identification of most important uncertaintysources is achieved by performing a global sensitivity analysis via computingthe so-called Sobol' indices. Such information aids in designing more efficientand targeted quality control procedures, which consequently may result inreducing the LIB production cost. An LiC$_6$/LiCoO$_2$ cell with 19 uncertainparameters discharged at 0.25C, 1C and 4C rates is considered to study theperformance and accuracy of the proposed UQ approach. The results suggest that,for the considered cell, the battery discharge rate is a key factor affectingnot only the performance variability of the cell, but also the determination ofmost important random inputs.

On Uncertainty Quantification of Lithium-ion Batteries: Application to an LiC$_6$/LiCoO$_2$ cell