Battery Load History Interpolation for Degradation Prediction
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Solution Overview
Problem
Existing battery diagnostic systems inaccurately predict future degradation states due to varying modes of degradation progression, leading to deviations from the actual situation.
Innovation Solution
A battery diagnostic system that includes a load history acquisition unit, interpolation processing unit, degradation estimation unit, and degradation prediction unit to estimate and interpolate missing data, allowing for accurate prediction of future degradation based on current conditions and predicted loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extrapolation method is used to predict future degradation state from current capacity degradation, then prediction can be obtained, but prediction accuracy deteriorates due to varying degradation modes
Solution Approach 1:
The patent segments the degradation prediction process into multiple independent prediction models, each dedicated to a specific degradation mode (capacity degradation, resistance degradation, etc.). This allows each model to specialize in one degradation type, improving overall prediction accuracy compared to a single extrapolation model that must handle all degradation modes generically.
Solution Approach 2:
The system dynamically determines which degradation mode is currently active by analyzing real-time battery data and operational conditions. Based on this dynamic assessment, the appropriate prediction model is selected and applied, allowing the system to adapt to changing degradation patterns rather than relying on static extrapolation assumptions.
2Loss of information
If interpolation processing is applied to estimate missing load history data, then data completeness is improved, but processing complexity increases
Solution Approach 1:
The patent introduces an interpolation processing unit as an intermediary component that automatically fills missing data gaps using neighboring data points and established patterns. This intermediary handles the complexity of data reconstruction internally, providing complete load history data to the prediction models without requiring complex manual intervention or system redesign.
Data Source
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AI summary
A battery diagnostic system (1) includes a load history acquisition unit (51a), an interpolation processing unit (51b), a degradation estimation unit (51c), a degradation prediction unit (51d), and an output unit (51e). The load history acquisition unit acquires a battery load history of a used secondary battery (25). When a portion of constituent data of the battery load history is missing, the interpolation processing unit estimates and interpolates the missing constituent data using the rest of the constituent data. The degradation estimation unit estimates, based on the battery load history, a present degradation state of the secondary battery and a cause of degradation that has brought about the degradation state. The degradation prediction unit performs diagnostics by predicting, using a predicted battery load, the present degradation state and the cause of degradation of the secondary battery estimated by the degradation estimation unit, a predicted degradation state of the secondary battery that will occur in the future upon having been used in the use mode. The output unit outputs the predicted degradation state of the secondary battery predicted by the degradation prediction unit.