Battery Health Prediction Using Verified Simulation Data

The patent creates a virtual copy of the battery system through numerical simulation models that replicate the physical battery's electrochemical behavior, thermal characteristics, and aging processes. This digital twin approach allows obtaining prediction data without conducting lengthy physical experiments, thus maintaining measurement precision while dramatically reducing time loss.

The patent replaces the mechanical/physical experiment system with a computational simulation system. Instead of conducting actual battery cycling tests that take months or years, the system uses numerical models solved by computers to predict battery behavior, substituting physical experimentation with mathematical simulation to achieve the same predictive goals efficiently.

The patent implements a feedback mechanism where numerical simulation results are continuously verified and calibrated against actual experimental data. The simulation model parameters are adjusted based on comparisons with measured battery behavior, ensuring that the time-efficient simulation maintains high reliability by learning from and adapting to real-world observations.

The patent performs preliminary verification of the numerical simulation model against experimental data before using it for predictions. By pre-calibrating and validating the simulation parameters using a subset of experimental data, the system ensures that subsequent time-efficient simulations will produce reliable predictions without needing to re-run lengthy experiments each time.

The patent develops a universal numerical simulation model that can predict the behavior of different battery cells with varying characteristics using a single framework. This multi-functional model accommodates cell-to-cell variations by adjusting input parameters rather than requiring separate management systems for each cell, thus maintaining reliability while reducing overall system complexity.

The patent manages individual cell variations by changing simulation parameters (such as initial capacity, internal resistance, aging rate) within the unified numerical model rather than creating separate management systems. This approach allows the system to adapt to different cell characteristics through parameter adjustment, simplifying the management architecture while maintaining accurate prediction capability for each cell.