Two-Headed Autoencoder for Rechargeable Battery Life Prediction

The patent replaces complex physics and chemistry models with a data-driven machine learning approach using a two-headed autoencoder. Instead of relying on detailed battery models that are difficult to implement, the system uses neural networks trained on discharge curve data to predict battery life, significantly simplifying the implementation while maintaining prediction accuracy.

The patent creates a simplified representation of battery behavior by training the autoencoder on discharge curve data. The model learns to replicate the essential patterns of battery degradation without needing to implement the full physical chemistry models, effectively copying the predictive capability in a simpler form.

The patent performs preliminary training of the autoencoder model on a first battery dataset before actual prediction is needed. This pre-training phase allows the system to learn patterns from historical data, enabling rapid predictions on new batteries without requiring extensive testing time during the actual selection process.

The patent uses a simplified data-driven model that can be quickly trained and applied, replacing the need for lengthy and expensive comprehensive testing procedures. The model provides reliable predictions with minimal time investment by leveraging patterns from previously tested batteries.