Battery Electrode Material Optimization Using AI Microstructure Simulation

The patent creates a digital twin (virtual copy) of the electrode microstructure that replicates the physical system's behavior. This digital model can be simulated repeatedly without consuming physical materials or requiring lengthy experimental setups, thus reducing experimental time while managing computational resources through efficient modeling approaches.

The patent systematically varies material parameters (such as fiber diameter, porosity, conductivity) in the digital twin model to optimize electrode performance. By changing parameters in the virtual model rather than physically reconfiguring experiments, the approach reduces experimental iterations while the computational efficiency is improved through focused parameter studies and surrogate modeling techniques.

The patent divides the electrode microstructure into representative volume elements (RVEs) that capture the essential features of the overall structure. By simulating only these representative segments rather than the entire electrode, the method maintains accuracy for predicting intercalation behavior while significantly reducing the computational domain and number of simulations required.

The patent performs simulations on a representative portion of the microstructure that contains sufficient detail to predict overall electrode behavior. This partial simulation approach provides adequate accuracy for optimization purposes without requiring exhaustive simulation of every microstructural feature, thus balancing precision with productivity.