Microbattery Classification Using Partial-Charge Impedance Signals
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Solution Overview
Problem
Current methods for testing microbatteries are time-consuming, potentially damaging, and unsuitable for 'Li-free' batteries, as they require complete charging and discharging cycles, which can initiate aging and make batteries non-marketable, and existing fast testing methods are not effective for 'Li-free' architectures.
Innovation Solution
A machine learning classification model is trained using measurements from partially charged batteries to predict functionality without the need for complete cycling, allowing for fast and non-damaging sorting of microbatteries into functional or defective categories, eliminating the need for threshold definitions and accommodating multiple dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete charging and discharging cycles are performed for testing microbatteries, then functionality can be accurately determined, but testing time becomes excessively long and batteries suffer aging damage
Solution Approach 1:
The patent applies preliminary action by performing only a partial charge (first charge operation) on the microbattery before testing, rather than completing full charge-discharge cycles. This preliminary charging step is sufficient to activate the battery's functionality for detection purposes, thereby dramatically reducing testing time while maintaining the ability to accurately determine battery functionality through subsequent impedance measurements.
2Reliability
If complete cycling is performed for testing, then battery functionality can be verified, but battery aging is initiated and marketability is reduced
Solution Approach 1:
The patent applies partial action by performing only a first charge operation to partially charge the microbattery, rather than completing full charge-discharge cycles. This partial charging is sufficient to verify battery functionality through impedance measurements without subjecting the battery to the harmful effects of complete cycling, thereby preventing aging and preserving battery marketability.
3Loss of time
If existing fast testing methods are used, then testing time is reduced, but they are not effective for 'Li-free' battery architectures
Solution Approach 1:
The patent applies parameter changes by utilizing electrochemical impedance spectroscopy measurements taken at specific frequencies (including 1 kHz and 10 Hz) to characterize the battery's functional state. By measuring impedance parameters at these specific frequencies after partial charging, the method achieves both fast testing and effectiveness for Li-free battery architectures, overcoming the limitations of existing fast testing methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid, non-destructive testing of microbatteries, preserving their marketability by predicting functionality based on partial charge measurements, reducing testing time, and avoiding the limitations of existing methods for 'Li-free' batteries.
Implementation Method 1
measuring a real part of an electrochemical impedance of the battery at a frequency of 1 kHz
Implementation Method 2
A microbattery is produced by successively depositing the following elements on a substrate: (i) a first current collector, (ii) a first electrode, (iii) an electrolyte layer, (iv) a second electrode, and (v) a second current collector
Data Source
AI summary
A computer-implemented method for the machine learning of a model for classifying batteries into two categories: functional or defective, the method comprising the following steps: acquiring, on a set of batteries of the same type, a group of measurements characteristic of the operation of a battery, carrying out complete cycling of each battery of the set and measuring at least one curve from among a charge curve or a discharge curve of the battery, determining, for each battery, a label of belonging to the functional or defective category by comparing at least one measured curve with a reference curve characterizing the correct operation of the battery, and carrying out supervised training of a model for classifying a battery according to the two categories based on the groups of measurements and on the label of belonging to one of the two categories.