cideMOD solves DFN physicochemical equations by Finite Element methods using FEniCS library. It enables doing physics-based battery simulations with a wide variety of use cases, from different drive cycles to studies of the SEI growth under storage conditions. Thermal and degradation models can be used to obtain more realistic predictions.

Physics-informed neural network trained to provide the evolution of lithium concentration in active material particles in both electrodes during a discharge process as well as the discharge curve of the full battery, based on the Single Particle Model.