Battery Cell Lifespan Prediction by Early Degradation Classification
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Solution Overview
Problem
Existing methods for predicting the lifespan of battery cells are time-consuming and costly, requiring discharge cycles up to 300 times to estimate capacity degradation.
Innovation Solution
A battery management apparatus and method that classify battery cells into classes based on degradation characteristics, using features such as initial values and change rates, to estimate capacity and predict lifespan more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery cells are discharged up to 300 cycles to check capacity degradation rate, then capacity estimation accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent extracts degradation features (capacity, resistance, impedance, temperature) from early battery cycles and uses machine learning models to predict lifespan before completing 300 cycles. This preliminary analysis of degradation patterns enables early prediction while maintaining accuracy.
Solution Approach 2:
The patent creates virtual copies of battery degradation patterns through machine learning models trained on historical data. These models simulate and predict future degradation behavior based on early-cycle observations, eliminating the need for actual 300-cycle testing of each cell.
2Measurement precision
If battery cells are discharged up to 300 cycles to check capacity degradation rate, then capacity estimation accuracy is improved, but cost increases significantly
Solution Approach 1:
The system performs preliminary degradation analysis during early battery cycles, extracting key features and feeding them into machine learning models for lifespan prediction. This approach maintains accuracy while reducing the number of required test cycles from 300 to a minimal number.
Solution Approach 2:
The patent uses machine learning models to create predictive copies of battery degradation patterns. These models are trained on historical data and can predict the lifespan of new battery cells based on early-cycle degradation features, eliminating the need for expensive 300-cycle testing of each individual cell.
3Measurement precision
If battery cells are classified into multiple classes based on degradation characteristics, then lifespan prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments battery cells into different degradation patterns or classes based on extracted features such as capacity degradation rate, resistance change, impedance evolution, and temperature characteristics. This segmentation enables more accurate lifespan prediction by accounting for different degradation behaviors.
Solution Approach 2:
The system monitors and analyzes changes in multiple battery parameters (capacity, resistance, impedance, temperature) throughout charging and discharging cycles. By tracking how these parameters evolve and change over time, the system can classify batteries into different degradation patterns and predict lifespan more accurately.
Data Source
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AI summary
Provided is a battery management apparatus including a measuring unit configured to measure a current and a voltage of battery cells, an analyzing unit configured to classify the battery cells into a plurality of classes based on a feature obtained from the current and the voltage of the battery cells and a degradation behavior of each of the battery cells, and a determining unit configured to determine a lifespan of the battery cell in a preset manner for each of the plurality of classes.