Battery SOC Estimation Using a Simplified Many-Particle Model

The positive electrode active material is divided into multiple particles, each representing a different reaction sequence. This segmentation allows the model to capture individual particle behavior while still using simplified calculations for each particle, balancing accuracy with computational efficiency.

The patent introduces a particle number as a new parameter to distinguish particles by their reaction sequence. By using this parameter to organize and categorize particle behavior, the model achieves higher SOC estimation accuracy without proportionally increasing computational complexity, as particles can be processed in groups or sequences.

The model segments particles into distinct groups based on reaction sequences, allowing efficient memory management. Instead of treating all particles uniformly, the segmentation enables reusable calculation patterns across particle groups, reducing overall memory requirements while maintaining high particle counts for accuracy.

The patent uses representative particles or particle groups to model the behavior of multiple particles with similar characteristics. By creating simplified representations (copies) of particle behavior patterns, the system can simulate thousands of particles without requiring proportional memory resources for each individual particle.