Validation of a Physics-Based Ion Transport Model for Ionic Liquid Electrolytes in Aluminum-Ion Batteries

Aluminum-ion batteries (AIBs) are promising energy storage solutions due to their high energy density, cost-effectiveness, and excellent safety attributed to aluminum's stability. Despite these advantages, challenges in electrolyte design and electrode optimization hinder widespread commercialization. These challenges include oxide film formation on the negative electrode [1], limited anion diffusion causing localized depletion [2], and electrolyte viscosity increase affecting IL conductivity during cycling [3].Among the diverse electrolyte options for AIBs, AlCl₃–[EMIm]Cl ionic liquid (IL) electrolyte emerges as promising because it has a robust electrochemical stability, is non-volatile, and inherently safe. Spectroscopic methods, including NMR and Raman spectroscopy, have confirmed the presence of critical AlCl₄⁻ and Al₂Cl₇⁻ ions in this electrolyte. These ions affect crucially the batteries' performance by presenting efficient ion transport [2, 4]. Therefore, advancing AIB technology requires a comprehensive understanding of IL electrolyte behavior through physics-based modeling and experimental validation is essential. The present study focuses on validating a physics-based Maxwell-Stefan model developed for ionic transport in the AlCl₃–[EMIm]Cl ionic liquid electrolyte used within symmetrical Al-ion cells. Key parameters that cannot be directly measured, including ionic diffusion coefficients and electrolyte conductivity, are obtained through mathematical optimization by fitting model simulations to experimental data. The validated model is then applied to gain deeper insight into ion transport dynamics and to guide electrolyte design optimization.Fig. 1a shows the comparison between a measured (blue symbols) and simulated (red line) voltage profile under an applied constant current pulse (orange line) of 10 min to a symmetric Al/Al cell with an AlCl₃–[EMIm]Cl IL. The simulation results demonstrate the model's capability to replicate real-cell performance accurately. The model is also able to highlight insights into the different loss processes in the IL, differentiating diffusion, migration, and Ohmic losses, shown in Fig. 1b. This comprehensive analysis reveals the electrolyte dynamics both during current loading and relaxation conditions. The parameterized electrolyte model developed in this work is a crucial first step toward full-cell AIB models.