Battery RUL Prediction Using Population Usage Correlations

The patent combines data from multiple batteries into a population-level dataset, aggregating usage information across many batteries to derive collective patterns. This merging approach allows the system to determine RUL for individual batteries without separately acquiring comprehensive BMS data for each one, reducing device complexity while maintaining measurement precision through population-based correlations.

The system performs preliminary analysis by training machine learning models on population-level battery data beforehand. This preliminary action creates pre-trained models that can quickly determine RUL for individual batteries without requiring complex real-time data acquisition, thus improving measurement precision while reducing the complexity and time of actual RUL determination operations.

By merging data from multiple batteries into a population dataset and training models collectively, the system eliminates the need for time-consuming separate data acquisition for each battery. The population-level analysis is performed once, and the resulting models can quickly assess individual batteries, significantly reducing assessment time while maintaining measurement precision.

The system performs preliminary model training on population data before actual RUL assessments. This preliminary action creates ready-to-use models that can rapidly determine RUL for individual batteries without requiring time-consuming separate data acquisition and analysis for each battery, thus reducing loss of time while preserving measurement precision.

The patent segments the analysis into two distinct levels: population-level analysis for model training and individual-battery analysis for RUL determination. The population-level analysis identifies general patterns and trains machine learning models, while the individual-battery analysis applies these models to specific batteries using their unique usage information. This segmentation allows the system to reduce device complexity through population-level generalization while maintaining measurement precision through individualized application.

The machine learning models serve as intermediaries between population-level analysis and individual battery assessment. These models are trained on population data to learn general RUL patterns, then act as mediators that apply these learned patterns to individual batteries based on their specific usage information. This intermediary approach allows the system to reduce complexity through population-level generalization while maintaining precision through individualized assessment.

The patent segments the assessment process into population-level model training and individual-battery RUL determination. This segmentation enables high productivity at the population level through batch processing and model training, while maintaining measurement precision at the individual level through specialized application of learned models to each battery's unique usage information.

Machine learning models act as intermediaries that enable high-productivity population-level analysis while preserving individual battery assessment precision. The models are trained efficiently on population data to learn general patterns, then serve as mediators that can quickly and accurately assess individual batteries by applying these learned patterns to specific usage information, thus achieving both high productivity and measurement precision.