Macro-scale battery models often rely on empirical electrolyte transport parameters, which limits their predictive capability under practical operating conditions such as fast charging, low temperature, high salt concentration, and long-term aging. This K2-SPRING project addresses this limitation by using molecular dynamics (MD) and density functional theory (DFT) calculations to investigate Li-ion transport mechanisms in liquid electrolytes at the molecular scale. The work focuses on how salt concentration, temperature, solvent composition, solvation structure, and ion-pair formation influence key electrolyte properties, including Li-ion diffusivity, viscosity, dielectric response, coordination number, and local ion–solvent interactions. These molecular-scale properties are then used to provide physics-based, condition-dependent transport parameters for macroscopic electrochemical models, supporting more reliable state-of-health prediction and safer battery-management-system design.

Macro-scale battery models often rely on empirical electrolyte transport parameters, which limits their predictive capability under practical operating conditions such as fast charging, low temperature, high salt concentration, and long-term aging. This K2-SPRING project addresses this limitation by using molecular dynamics (MD) and density functional theory (DFT) calculations to investigate Li-ion transport mechanisms in liquid electrolytes at the molecular scale. The work focuses on how salt concentration, temperature, solvent composition, solvation structure, and ion-pair formation influence key electrolyte properties, including Li-ion diffusivity, viscosity, dielectric response, coordination number, and local ion–solvent interactions. These molecular-scale properties are then used to provide physics-based, condition-dependent transport parameters for macroscopic electrochemical models, supporting more reliable state-of-health prediction and safer battery-management-system design.