Battery Lifetime Prediction Using Temporary Capacity Recovery Data Correction
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Solution Overview
Problem
Existing methods for predicting battery lifetime are prone to large errors due to the intermittent temporary capacity recovery characteristic of batteries, which is not properly considered in charge/discharge test data.
Innovation Solution
An apparatus and method that determine a temporary capacity recovery section in battery charge/discharge test data, create alternative data by replacing or calibrating this section, and analyze trends using moving averages or other methods to predict battery lifetime more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery lifetime is predicted using conventional charge/discharge test data, then prediction can be performed, but large errors occur due to temporary capacity recovery characteristics
Solution Approach 1:
The patent extracts and removes temporary capacity recovery sections from charge/discharge test data. The capacity recovery section determination unit identifies these intermittent recovery portions, and the trend analysis unit creates alternative data by replacing or calibrating them, effectively taking out the harmful temporary recovery characteristics that cause prediction errors.
Solution Approach 2:
The patent changes the parameters of test data by creating alternative data through replacing or calibrating temporary capacity recovery sections. This parameter transformation converts problematic original data into corrected alternative data that reflects true battery degradation trends, improving prediction accuracy.
2Measurement precision
If temporary capacity recovery sections are identified and removed from test data, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the charge/discharge test data into distinct sections, identifying temporary capacity recovery portions separately from normal degradation sections. This segmentation allows targeted processing of only the problematic recovery sections rather than processing entire datasets, managing complexity while improving accuracy.
Solution Approach 2:
The trend analysis unit acts as an intermediary that creates alternative data by replacing or calibrating temporary capacity recovery sections. This intermediary process transforms original problematic data into corrected alternative data that can be used for accurate prediction without requiring complete redesign of the prediction system.
Data Source
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AI summary
The present invention relates to an apparatus for predicting battery lifetime that may predict battery time more accurately by removing errors due to a characteristic of recovering temporary capacity happening intermittently from charge/discharge test data of a battery and a method thereof. The apparatus for predicting battery lifetime in accordance with the present invention comprises: a capacity recovery section-determining unit for determining a temporary capacity recovery section by analyzing charge/discharge test data of a battery; a trend analysis unit for creating alternative data based on test data before and after the temporary capacity recovery section determined by the capacity recovery section-determining unit, and analyzing trend of the test data by reflecting the alternative data; and a lifetime predicting unit for predicting lifetime of the battery based on data outputted from the trend analysis unit.