Method for sorting a set of batteries based on a machine classification model
View Patent ↗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.
1 . A method for supervised training of a machine learning model for classifying batteries into two categories: functional or defective, the method comprising the following steps of:
measuring, on a set of batteries of a same type, a group of measurements characteristic of an operation of a battery,
executing a complete cycling of each battery of said set and measuring at least one curve from among a charge curve or a discharge curve of the battery,
comparing at least one measured curve with a reference curve characterizing a correct operation of the battery,
determining, for each battery, a label of belonging to a functional category or a defective category based on the comparison,
executing, on a processor, a supervised training of a model for classifying a battery according to the two categories, the model receiving as input the groups of measurements and the label of belonging to one of the two categories, and
providing the model for use in a manufacturing process of batteries.
2 . The machine learning method according to claim 1 , wherein the supervised training of the classification model is carried out by way of a random forest algorithm.
3 . The machine learning method according to claim 1 , wherein the group of measurements characteristic of the operation of a battery comprises at least one measurement of an open-circuit voltage across terminals of the battery.
4 . The machine learning method according to claim 1 , wherein the group of measurements characteristic of the operation of a battery comprises at least one measurement of a profile of a current flowing through the battery during a partial charge.
5 . The machine learning method according to claim 4 , wherein the current profile is measured with at least one particular point from among a maximum current value and an end-of-partial-charge current value.
6 . The machine learning method according to claim 1 , wherein the group of measurements characteristic of the operation of a battery comprises at least one measurement of a profile of a voltage across terminals of the battery during relaxation of the battery following a partial charge.
7 . The machine learning method according to claim 6 , wherein the voltage profile measurement comprises at least one estimate, performed through linear regression, of a slope of a voltage curve during discharging and/or measuring an end-of-relaxation potential.
8 . The machine learning method according to claim 1 , wherein the group of measurements characteristic of the operation of a battery comprises at least one electrochemical impedance spectroscopy measurement carried out by exciting the battery with a signal with at least one predetermined frequency.
9 . The machine learning method according to claim 8 , wherein the electrochemical impedance spectroscopy measurement is carried out for at least two frequencies predetermined at 1000 Hz and 10 Hz.
10 . The machine learning method according to claim 1 , wherein the group of measurements characteristic of the operation of a battery comprises at least one measurement of a position of a battery on a fabrication wafer.
11 . The machine learning method according to claim 1 , wherein the batteries are Li-free solid batteries.
12 . A computer-implemented classification model trained using the machine learning method according to claim 1 .
13 . A method for testing a set of batteries comprising the classification model according to claim 12 , the method for testing further comprising the following steps, for each battery:
receiving a battery to be tested in a manufacturing process,
measuring, on the battery, the group of measurements,
executing, on a processor, the trained classification model to determine, based on said group of measurements, whether the battery belongs to the functional or defective category, and
if the battery belongs to the defective category, eliminating the battery from a manufacturing process.
14 . A non-transitory computer-readable storage medium storing a computer program comprising code instructions for implementing the method according to claim 1 .
15 . A device for testing a set of batteries, comprising a measurement apparatus (Vn, An) and a computing device (Cn) that are configured together to execute the test method according to claim 13 .
16 . The machine learning method of claim 3 wherein the group of measurements characteristic of the operation of a battery comprises at least an average measurement over a predetermined time interval.