The battery capacity changes over time and over repeated discharging and charging cycles. Capacity fading could be caused by many different mechanisms such as overcharging, growth of solid-electrolyte interphase (SEI) due to undesirable side reactions, mechanical cracks, and so on. Ansys Fluent uses both empirical and physics-based models.

For the emperical models, you can consider the battery capacity fade over both long and short time periods. To estimate long-term battery aging, the emperical battery life model is used. The capacity fade effect is treated as a pre-processing procedure in CFD, in which the usable capacity is updated before a CFD simulation. To estimate short-term battery aging, the battery capacity fade model is used. Here, the capacity fade can be specified on-the-fly as a function of temperature and C-rate.

The physics-based model incorporates the fundamental aging mechanisms into the Newman's P2D model. The model allows you to predict the battery aging process during a large number of electric load cycles in the standalone mode. You can further consider the aging effects in CFD simulations as a preprocessing procedure by plugging in the standalone simulation results as steady state profiles.