Secondary Battery Low-Voltage Prediction Using Transfer Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting low-voltage failures in secondary batteries are time-consuming and lack accuracy, requiring extensive data and being inefficient in adapting to changing manufacturing conditions.
Innovation Solution
An apparatus and method using machine learning to generate and optimize low-voltage prediction models by selecting main factors from training data, applying transfer learning to reduce data collection time and improve accuracy across different battery conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use extensive data collection and traditional analysis approaches to determine low-voltage failure criteria, then measurement precision may be improved, but loss of time increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models using historical battery data during manufacturing. This pre-training establishes baseline low-voltage failure criteria before actual product deployment, eliminating the need for extensive real-time data collection and analysis when determining failures in production or usage.
Solution Approach 2:
The patent replaces traditional mechanical/data-intensive analysis methods with machine learning algorithms. Instead of manually analyzing extensive voltage data sets using conventional statistical methods, the system uses trained ML models to automatically predict low-voltage failures, significantly reducing analysis time while maintaining or improving accuracy.
2Measurement precision
If traditional methods collect and analyze large amounts of battery data to establish failure criteria, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system performs data processing and model training in advance during manufacturing phases. By pre-establishing failure criteria through offline data analysis and model training, the actual deployment system requires minimal computational resources and simplified processing, reducing device complexity while maintaining high measurement precision.
Solution Approach 2:
The patent creates simplified copies or representations of complex battery behavior through trained machine learning models. These models capture essential failure patterns without requiring the full complexity of original data processing systems, enabling accurate failure determination with reduced computational overhead.
3Ease of operation
If existing methods use fixed determination criteria for low-voltage failure, then ease of operation is improved, but adaptability decreases when manufacturing conditions change
Solution Approach 1:
The patent implements dynamic adaptability by designing machine learning models that can be retrained and updated as manufacturing conditions change. Unlike fixed thresholds, these models adapt to new production environments, battery chemistries, or operational conditions while maintaining ease of operation through automated prediction processes that require minimal manual intervention.
Solution Approach 2:
The system changes parameters by using machine learning models with adjustable parameters that can be optimized for different manufacturing conditions. When production conditions change, the model parameters can be retrained on new data, providing adaptability while maintaining the simplicity of automated failure determination.
4Reliability
If traditional methods require extensive data collection to determine failure criteria, then reliability of failure prediction is improved, but productivity decreases due to time consumption
Solution Approach 1:
The patent performs extensive data analysis and model training in advance during manufacturing or setup phases. This preliminary action builds reliable prediction models that can then rapidly assess battery health in production or usage, achieving both high reliability through comprehensive training data and high productivity through fast inference.
Solution Approach 2:
The system pre-processes and pre-trains models using historical data, storing the learned patterns for rapid deployment. This allows the system to maintain high reliability by having been trained on extensive data while achieving high productivity during actual operation by simply applying the pre-trained models without reprocessing the original data sets.
Data Source
AI summary
The present invention relates to an apparatus and a method of predicting a low-voltage failure of a secondary battery.