Battery RUL Estimation via Degradation Path Classification
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Solution Overview
Problem
Current methods for determining the remaining useful life (RUL) of military aircraft batteries are inadequate, as they often fail to produce accurate estimates or relate degradation to RUL effectively, especially in real-world applications with complex failure modes and uncertain thresholds.
Innovation Solution
A path classification and estimation (PACE) system and method that classifies battery degradation and estimates remaining useful life by transforming observed degradation data into functional approximations, computing similarities with exemplar paths, and using these similarities to predict Capacity RUL and Cycle RUL.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current RUL determination methods are used, then simplicity is maintained, but accuracy and reliability of RUL estimates deteriorate
Solution Approach 1:
The system segments the RUL estimation process into distinct functional modules: data acquisition module that collects battery parameters, prognosis module that performs degradation analysis, and output module that generates RUL estimates. This segmentation allows each module to be optimized independently while maintaining overall system accuracy without excessive complexity
Solution Approach 2:
The system introduces intermediate degradation indicators and state-of-health metrics as mediators between raw battery data and final RUL estimates. These intermediaries transform complex battery degradation patterns into manageable analysis stages, improving estimation accuracy while keeping the system architecture tractable
2Adaptability or versatility
If degradation data is not classified and compared with exemplar paths, then system simplicity is maintained, but ability to handle complex failure modes and uncertain thresholds deteriorates
Solution Approach 1:
The system performs preliminary classification of degradation data into distinct failure mode categories before detailed analysis. By pre-categorizing degradation patterns and comparing them with exemplar paths of known failure modes, the system adapts to complex failure scenarios without requiring complex real-time processing, thus handling versatility while controlling processing complexity
Solution Approach 2:
The system transforms raw degradation data into standardized parameters and metrics that can be directly compared across different failure modes. By changing the parameter representation of degradation data and using similarity comparisons with exemplar paths, the system achieves adaptability to various failure modes while maintaining manageable data processing complexity through parameter standardization
3Measurement precision
If dynamic tracking of asset behavior changes is implemented, then RUL estimation accuracy is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system implements periodic updates of degradation analysis and RUL estimation rather than continuous real-time processing. By periodically re-evaluating battery state and comparing with exemplar paths at scheduled intervals, the system maintains accurate dynamic tracking of asset behavior while reducing computational energy consumption compared to continuous monitoring approaches
Data Source
AI summary
Path classification and estimation method and system used in combination with a computer and memory for prognosticating the remaining useful life of an in-service battery asset by classifying a present degradation path of the in-service battery asset as belonging to one or more of previously collected degradation paths of one or more exemplary battery assets and using the resulting classifications to estimate the remaining useful life of the in-service battery asset thereby transforming raw data inputs into actionable state-of-health outputs.