Probabilistic Sensitivity Analysis of a Nonlinear Electrochemical Model as a Virtual Replica for Lithium-Ion Battery Design Under Uncertainty

This paper presents a probabilistic sensitivity analysis of a nonlinear electrochemical model for lithium-ion batteries. The model is treated as a reduced virtual replica for uncertainty-aware analysis rather than as a full digital twin. A reduced electrochemical formulation is combined with constrained inverse parameter identification using experimental current–voltage data to relate observable battery behavior to effective model parameters. Predictive variability is assessed through Monte Carlo uncertainty propagation and global sensitivity analysis under both charging and discharging conditions. The results indicate that the particle radius of the positive active material and the effective electrodes area are the dominant contributors to terminal-voltage uncertainty, whereas the electrode thickness parameter and negative electrode active material particle radius have a moderate influence within the studied ranges. Rank-based and variance-based sensitivity measures are more informative than linear indices for this reduced nonlinear system. From a mathematical perspective, the work integrates reduced-order modeling, inverse problem formulation, numerical simulation, and uncertainty quantification in one computational framework for battery analysis. The results support uncertainty-aware parameter prioritization, calibration of reduced electrochemical models, and provide a basis for future work on battery design, control, and digital-twin-oriented extensions under uncertainty.