Battery Life Prediction With Global-Local Decomposition Transformer
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Solution Overview
Problem
Current methods for predicting battery life in vehicles require accurate physical configurations and are costly or inaccurate under uncertain environmental conditions, especially for Li-ion batteries in electric vehicles.
Innovation Solution
A battery life prediction system using a global-local decomposition transformer that decomposes battery information into local and global parts, allowing for faster training and more accurate predictions by reusing information across batteries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current physical configuration-based methods are used for battery life prediction, then prediction accuracy may be maintained under controlled conditions, but the system becomes costly to implement and inaccurate under uncertain environmental conditions
Solution Approach 1:
The patent segments battery information into local parts (battery-specific features) and global parts (common patterns across batteries) using a global-local decomposition transformer. This segmentation allows the system to reuse global patterns across multiple batteries while maintaining accuracy for individual battery predictions, reducing implementation cost while preserving prediction accuracy.
Solution Approach 2:
The patent copies and reuses global parts entries across different battery datasets. By identifying and copying common patterns from one battery's data to another's prediction model, the system reduces the need for extensive physical configuration data and costly implementation while maintaining prediction accuracy through pattern reuse.
2Measurement precision
If traditional battery life prediction methods are used, then comprehensive analysis may be achieved, but training time becomes excessively long
Solution Approach 1:
The patent performs preliminary action by pre-processing battery data into structured formats with separate local and global parts entries before actual prediction. This preliminary organization of data allows for faster training during the prediction phase, as the transformer model can efficiently access and process pre-structured information rather than raw data.
Solution Approach 2:
By segmenting battery information into local and global components, the system enables selective processing during training. The global parts can be reused across multiple predictions, reducing redundant training computation and significantly decreasing training time while maintaining comprehensive analysis capability.
3Measurement precision
If physical configuration data is collected for each battery, then prediction accuracy may be improved, but the system becomes too costly to implement
Solution Approach 1:
The patent creates a universal global parts entry that can be applied across multiple batteries and datasets. This universal component captures common degradation patterns and environmental responses that are applicable to different battery instances, reducing the need for extensive battery-specific physical configuration data while maintaining prediction accuracy.
Solution Approach 2:
The system copies global patterns from one battery dataset to another, reducing the need to collect and process extensive physical configuration data for each individual battery. This copying mechanism allows accurate predictions using less costly data collection approaches.
Data Source
AI summary
Systems and methods described herein relate to implementing battery life prediction strategies. In one embodiment, a method includes receiving a first battery dataset containing a first set of battery entries, a first set of historical usage entries, a first set of local parts entries, and a first global parts entry; generating a second battery dataset containing a second set of battery entries and a second set of historical usage entries; generating a second set of local parts entries for the second battery dataset based on comparing the first set of historical usage entries with the second set of historical usage entries; copying the first global parts entry to a second global parts entry of the second battery dataset; and optimizing via one or more global local decomposition transformers the second set of local parts entries and the second global parts entry based on the second set of historical usage entries.