After completing our single cell testing, we have a lot of data to go through. Hopefully this page will help guide people in going through the data. If you have any questions, contact Micah Black. The tests chosen were guided by the Individual Cell Testing Evaluation page.
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Open Circuit Voltage
| Priority | 1 |
|---|---|
| File Name | cell#-OcvTestScenario.csv |
| Results Status | Completed |
| Visualization Status | Completed |
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DC Internal Resistance Pulses
| Priority | 4 |
|---|---|
| File Name | cell#-DcCurrentPulseScenario.csv |
| Results Status | Not Completed |
| Visualization Status | Not Completed |
The DC Internal Resistance Pulse test is very common as it is super easy to do (maybe not so accurately) with a multimeter and a power resistor. However, there are many complications with this test.
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DC Internal Resistance Slope
| Priority | 3 |
|---|---|
| File Name | cell#-DcLinearSweepScenario.csv |
| Results Status | Completed |
| Visualization Status | Completed |
This measurement is similar to determining the resistivity of a sample of material, but applied to a battery cells. We applied a changing current from -3A all the way to 3A and measured the voltage of the cell. This gives a fairly linear response, but would maybe be better split up in to charge and discharge sections, or possibly a quadratic would fit the data better.
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AC Internal Impedance (1kHz)
| Priority | 2 |
|---|---|
| File Name | cell#-AcInternalScenario.csv |
| Results Status | Completed |
| Visualization Status | Not Completed |
The AC Internal Impedance test at 1kHz is an industry standard test for measuring the internal resistance of cells, and is usually the value that manufacturers put an upper limit on. (LG says <40mOhm no the MJ1 datasheet I believe). The AC test produces different results than the DC methods as the AC method also takes into account the inductance and capacitance of the cell, and some of the other complexities of the equivalent circuit model. Essentially, the voltage never has time to fully settle out.
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Capacity Ration
| Priority | 6 |
|---|---|
| File Name | cell#-TenSecondsDischargeScenario.csv |
| Results Status | Not Completed |
| Visualization Status | Not Completed |
I think that this test is super cool and has the possibility to give some super interesting results.
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Weight (Possible Indication of Capacity)
| Priority | 5 |
|---|---|
| File Name | cell#-weight.csv |
| Results Status | Completed |
| Visualization Status | Completed |
Some of the papers linked on the Individual Cell Testing Evaluation page found a correlation between weight and capacity, while others did not. For us, this data is just another source of outlier identification.
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Sample data for the weight measurements is shown in the graph above. The data will start with either the old cell on the scale, or no cell on the scale. If the old cell was on the scale, it is removed, and then the new cell is put on the scale. There will be measurement errors, so zoom in on the data in MatPlotLib to see what you're dealing with, remove outliers, and average it.
Data:
All testing result data can be found here in the Single-Cell-Testing-Results zip file: https://drive.google.com/drive/u/2/folders/19MzeWnzwiVnc4nDDM7SIhcqjXieO3bOU
Warning - We have a lot of data - The zip file is 500MB, and extracted it is over 6GB.
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Data Processing Scripts:
Here is the python file used to generate the AC-IR values. It is a bit of a mess. It should be run from the same file as the data is in as of right now, but can definitely be improved.
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