Electrode microstructure governs rate-dependent capacity retention: A metrology-aware optimization framework for sodium-ion batteries

Electrochemical optimization can greatly improve battery performance, but optimized designs may still be fragile under realistic measurement noise and manufacturing variability. Here, we present a measurement-uncertainty-integrated optimization framework for a cylindrical 18650 sodium-ion cell that embeds instrument precision into a Doyle–Fuller–Newman model via a three-tier sensitivity analysis: ( i ) instrument-resolved local perturbations that map laboratory measurement errors to discharge-capacity deviations; ( i i ) one-factor-at-a-time global screening over physically admissible domains to identify influential design variables; and ( i i i ) Sobol variance-based global sensitivity analysis that quantifies first-order and higher-order interaction effects. Using this framework, we find that global multi-parameter optimization yields a design with nearly rate-independent discharge capacity of about 6 mA⋅h over 0.5 – 3 C, unlike the baseline cell whose capacity drops from 2.7 mA⋅h to 0.9 mA⋅h over the same range. A local robustness assessment around the optimum defines admissible parameter intervals and shows that cathode thickness and porosity become most sensitive to measurement uncertainty. The framework also specifies allowable particle-size ranges of 0.2 – 4.54 µm for the anode and 0.2 – 3.79 µm for the cathode. Sobol indices indicate a rate-dependent shift in dominant variance sources — from electrode thickness at low C-rates to particle radii at high C-rates — with strong interactions, particularly between anode thickness and anode particle radius. These couplings demonstrate that independent parameter-by-parameter tolerance settings cannot ensure robust performance. The framework links electrochemical cell design to metrological requirements, delivering quantitative tolerance windows and a systematic path to reproducible, high-capacity sodium-ion cells.