Differentiable Battery Selection for Application-Specific Performance
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Solution Overview
Problem
The selection of lithium-ion batteries for specific applications is resource-intensive and time-consuming due to the reliance on heuristic methods and prolonged testing, which does not effectively account for application-dependent performance metrics such as energy, power, and cycle life.
Innovation Solution
A system and method utilizing a continuous latent space representation of battery performance, paired with a differentiable modeling block for direct mapping of performance metrics to specific application scenarios, allowing for efficient selection and potential parameter adjustments of lithium-ion batteries based on autoencoder-derived latent spaces and application profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristic methods and prolonged battery testing are used for battery selection, then selection accuracy may be improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent creates a virtual copy of the battery testing process through simulation. Instead of physically testing batteries in real applications, the system uses computational models to simulate battery performance under various conditions, extracting latent features that predict actual performance without requiring prolonged physical testing.
Solution Approach 2:
The system performs preliminary analysis by encoding battery specifications into latent spaces and pre-computing performance predictions before actual application deployment. This allows the most suitable batteries to be identified in advance through computational evaluation rather than through time-consuming sequential testing.
2Ease of manufacture
If heuristic methods are used for battery selection, then implementation simplicity is maintained, but selection accuracy and application-dependency decrease
Solution Approach 1:
The patent replaces traditional heuristic evaluation methods with a computational modeling system. The differentiable performance model automatically evaluates battery specifications against application requirements using latent space comparisons, substituting manual or rule-based assessment with automated computational analysis that provides both accuracy and systematic evaluation.
3Adaptability or versatility
If traditional battery testing is conducted for multiple application scenarios, then comprehensive performance data is obtained, but resource consumption and testing duration increase
Solution Approach 1:
The patent creates a universal performance evaluation framework that can assess battery suitability for multiple different applications simultaneously. The differentiable performance model takes application-specific parameters as input and can evaluate the same battery against various application scenarios without requiring separate physical testing campaigns for each application type.
Solution Approach 2:
The system changes the evaluation parameters from physical testing conditions to computational latent space representations. By encoding both battery specifications and application requirements into comparable latent features, the system can evaluate performance across multiple applications by varying input parameters rather than conducting separate physical tests for each scenario.
Data Source
AI summary
Disclosed herein is a system and method for selecting a battery for a particular application, for example, batteries used in portable electronics, electric vehicles, satellites, etc. The method uses an end-to-end differentiable modeling approach that allows the selection of batteries directly from the parameters of the battery and a specification of the particular application for which the batteries are being selected.