Battery Thermal Analysis Model Using GAN-Generated Training Data

The patent pre-processes thermal analysis data using numerical methods to create a training dataset, then uses this pre-processed data to train a neural network model. The model is constructed in advance with pre-computed thermal analysis results, enabling rapid prediction without performing full numerical analysis during actual use. This preliminary action transfers the computational burden from runtime to model training time.

The patent creates a virtual copy of the thermal analysis system by training a neural network model to replicate the behavior of complex numerical thermal analysis. The model learns to copy the input-output relationships of the governing equation-based analysis, allowing it to produce similar results much faster. The neural network essentially creates a simplified digital twin of the thermal analysis process.

The patent performs the computationally intensive numerical analysis in advance to build a training dataset, then uses this pre-computed data to train a neural network model. The model construction phase consolidates the computational work, allowing subsequent thermal analysis queries to be answered with minimal computational resources. This shifts the energy burden from repeated analysis to one-time model training.

The neural network model creates a simplified computational copy of the complex thermal analysis system. Instead of repeatedly solving governing equations with high computational cost, the model uses learned patterns from the training data to generate thermal analysis results with fraction of the original computational resources. The model copies the essential thermal behavior without requiring the full computational machinery.

The patent replaces the complex system of governing equations and numerical solvers with a simpler neural network model that has been trained to replicate thermal analysis behavior. The model structure is inherently simpler than full numerical thermal analysis systems, using learned patterns instead of complex mathematical formulations. This copying approach maintains accuracy while reducing system complexity.

The patent substitutes the mechanical/mathematical system of governing equations and numerical methods with an information-processing system based on neural networks. Instead of solving differential equations through computational mechanics, the system uses machine learning to predict thermal behavior directly from input parameters, replacing complex mathematical machinery with a data-driven model.