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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I would love to have a graph generated for every single data point for every single cell. The Visualization Status is for this - do we have a graph of every cell for this measurement, as well as a graph of the distribution of the results (plot the result from every cell on a graph).
Open Circuit Voltage
| Priority | 1 |
|---|---|
| File Name | cell#-OcvTestScenario.csv |
| Results Status | Not Completed |
| Visualization Status | Not Completed |
These cells have been sitting for almost a year since being manufactured before we tested them. All cells have what is called a self-discharge current, where the cell gradually discharges itself. To determine the self discharge current cells are usually left sitting and their voltage periodically monitored. Any cells that diverge in voltage quicker than other cells have a high self discharge current.
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Due to some measurement error and inaccuracies in controlling super low currents, the current measured by the SMU is jumps around a bit. We need to remove the measurements that current or voltage was an outlier, and then create somewhat of an average of the rest of the measurement with more weight being given to the voltage measurements taken when the current was closer to 0 - there is probably a more technical term for this, but you get the point. The distribution of sample data is below. Graphs were made in MatPlotLib in Python.
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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Note on this graph (the third graph), the bottom axis should read pulse number, and not amplitude of current. These pulses were all done at the same current.
DC Internal Resistance Slope
| Priority | 3 |
|---|---|
| File Name | cell#-DcLinearSweepScenario.csv |
| Results Status | Not Completed |
| Visualization Status | Not 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.
The slope of the line on a voltage vs current curve gives you the internal resistance of the cell. It is essentially a DC pulse measurement, but average over all amplitudes of pulses.
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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The internal impedance is calculated by dividing the amplitude of the voltage by the amplitude of the current. We used MatPlotLib and python to fit our data to sinusoidal profiles and extract the parameters, then got our results.
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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We can compare the capacity obtained from the reference cell to the capacity obtained from the 10 second discharge during this region, and get a capacity estimate for the cell.
Weight (Possible Indication of Capacity)
| Priority | 5 |
|---|---|
| File Name | cell#-weight.csv |
| Results Status | Not Completed |
| Visualization Status | Not 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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