Model‐Based Uncertainty Quantification for the Product Properties of Lithium‐Ion Batteries

Abstract

A model‐based uncertainty quantification (UQ) approach is applied to the manufacturing process of lithium‐ion batteries (LIB). Cell‐to‐cell deviations and the influence of sub‐cell level variations in the material and electrode properties of the cell performance are investigated experimentally and via modeling. The electrochemical battery model of the Doyle–Newman type is extended to cover the effect of sub‐cell deviation of product properties of the LIB. The applied model is parameterized and validated using a stacked pouch cell containing Li(Ni1/3Co1/3Mn1/3)O2 (NMC) and graphite (LixC6). It is integrated into a sampling‐based UQ framework. A nested point estimate method (PEM) is applied to a large number of independent normal distributed parameters. The simulations follow two consecutive nonideal manufacturing process steps: coating and calendering. The nested PEM provides a global sensitivity analysis that shows a change in sensitivity of the investigated parameters depending on the applied C‐rate. Furthermore, the sub‐cell level deviation of parameters in heterogeneous electrodes provokes a nonuniform current distribution in the cell. This alters the variance of the discharge capacity distribution. Therefore, sub‐cell deviation has to be considered to quantify process uncertainties. The applied method is feasible and highly efficient for this purpose.

Impact of Particle Size Distribution on Performance of Lithium‐Ion Batteries

“…For example, calendering influences the porosity and thickness of the electrode. [12][13][14][15][16][17] The thickness of the electrode influences the diffusion pathway, because the thicker the electrode, the longer the diffusion length for ions. [15] Similar behavior applies to particle size and PSD, as they impact electrolyte diffusion pathway through tortuosity and porosity, also the solid diffusion.…”

“…[12][13][14][15][16][17] The thickness of the electrode influences the diffusion pathway, because the thicker the electrode, the longer the diffusion length for ions. [15] Similar behavior applies to particle size and PSD, as they impact electrolyte diffusion pathway through tortuosity and porosity, also the solid diffusion. [18][19][20] High overpotential due to solid diffusion and interface resistance reduce energy efficiency and may cause safety hazards.…”

“…Aside from particle size and PSD, there are still many other process steps during electrode production that affect the properties of batteries. For example, calendering influences the porosity and thickness of the electrode [12–17] . The thickness of the electrode influences the diffusion pathway, because the thicker the electrode, the longer the diffusion length for ions [15] .…”

A Comprehensive Physical‐Based Sensitivity Analysis of the Electrochemical Impedance Response of Lithium‐Ion Batteries

“…[58] A variation of lithium concentration in the electrolyte (c e,0 ) affects the entire impedance spectrum, because it determines the value of the ionic conductivity and the lithium diffusion coefficient of the electrolyte (Figure 6e). The HFR has its minimum value at 1200 mol m À3 , which is the optimal concentration that maximizes the ionic conductivity (Equation (11)). Nevertheless, the overall variation of the HFR is relatively small if compared with the effect of other parameters.…”

“…Usually, LIB characterization is limited to the analysis of charge and discharge curve and/or of the pulse‐relaxation behavior, where the degrees of freedom are limited to environmental condition, protocol C‐rate, and average state‐of‐charge (SOC) of the battery. [ 9–11 ] However, such measurements are limited in terms of poor insights from a diagnostic point‐of‐view, especially if used singularly, resulting in the inability of distinguishing many phenomena interplaying during battery operation. [ 12 ]…”