Battery Test System Predicting Results Using Partial Data
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Solution Overview
Problem
Standardized battery tests for automotive batteries can take weeks or months to complete, leading to delayed feedback for design and manufacturing processes, which can hinder timely adjustments and compliance with regulatory standards.
Innovation Solution
A battery testing system that uses a predictive model based on partial test data to forecast test outcomes, allowing for early prediction of battery performance and reducing the duration of testing, utilizing a computer-readable medium and processor-executable instructions to analyze historical data and provide predictions on battery characteristics like discharge capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized battery tests are conducted for extended periods to ensure accurate lifetime performance measurement, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary analysis of battery test data at intermediate stages to predict final outcomes. By analyzing discharge capacity, charge capacity, and efficiency metrics during the test process, the system can forecast whether the battery will meet lifetime performance requirements before the complete test duration elapses, thus reducing the effective testing time required while maintaining measurement accuracy.
Solution Approach 2:
The system continuously monitors battery test parameters and provides feedback through predictive analytics. By comparing real-time test data against historical patterns and performance thresholds, the system can determine with high confidence whether a battery will pass or fail lifetime performance requirements, enabling early termination of tests for clearly failing units and reducing overall testing time while preserving measurement precision.
2Reliability
If full-duration standardized tests are completed to obtain reliable battery performance data, then reliability of test results is improved, but productivity decreases
Solution Approach 1:
The system applies partial action by analyzing only the most critical test parameters (discharge capacity, charge capacity, efficiency) at strategically selected intermediate time points rather than requiring complete test duration. This partial analysis provides sufficient reliability for design decisions while dramatically improving productivity by enabling earlier feedback in the development cycle.
Solution Approach 2:
The system replaces the mechanical time-based testing process with a computational prediction model. Instead of waiting for physical test completion, the system uses machine learning algorithms and historical data patterns to predict final outcomes from intermediate measurements, substituting computational analysis for extended physical testing and thereby improving productivity while maintaining result reliability.
Data Source
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AI summary
A method of predicting battery test results includes using a battery test computer to predict a battery test result for a battery undergoing testing. The battery test computer comprises a tangible, non-transitory computer-readable medium storing a battery test management system implemented as one or more sets of instructions. The battery test management system includes a predictive module configured to predict the battery test result using less than all data required for the battery test to be complete, a validation module configured to validate the prediction, and a training module configured to re-train the predictive module based on results generated by the validation module. The battery test computer also includes processing circuitry configured to execute the one or more sets of instructions, and outputting, via a user interface, the prediction of the result and a confidence level associated with the prediction.