Battery Diagnosis via Overpotential-Corrected Full-Cell Profiles

A machine learning model acts as an intermediary between the high electric stimulation measurement and the final diagnosis result. The model learns the relationship between high-rate and low-rate charge/discharge characteristics from training data, then uses this learned relationship to correct the high-rate measurement results, effectively translating noisy high-rate data into accurate low-rate equivalent diagnoses.

The patent changes the electric stimulation rate parameter from low (accurate but slow) to high (fast but noisy), then uses a machine learning model to adjust and correct the measurement parameters. The model learns optimal correction factors that transform high-rate measurement characteristics into accurate low-rate equivalent results, achieving both speed and accuracy.

The machine learning model serves as an intermediary that processes fast high-rate measurements and transforms them into accurate diagnostic results. By training on paired high-rate and low-rate data, the model learns to predict accurate low-rate equivalent results from high-rate inputs, eliminating the need for slow direct low-rate measurements.

The machine learning model is pre-trained offline using comprehensive low-rate measurement data from multiple batteries. This preliminary training phase captures the complex relationships between different charge/discharge rates and battery states, enabling the model to quickly accurate diagnose new batteries using only fast high-rate measurements without requiring time-consuming low-rate measurements at runtime.