Self-discharge measurements: How external factors impact results
I was set to present a topic in person at the Novi Battery Conference in September, in Novi, Michigan. Things were looking good up until mid-August, and then the live event was finally cancelled due to the pandemic. However, the show must go on! The organizers opted to do a virtual event, which took place this past November. As the number of presentations were reduced, I count myself as being one of the lucky ones who they decided to retain for the virtual event. The title of my presentation was “Achieving valid self-discharge measurements: How external factors impact results.” This was a nice sequel to what I presented at the Novi Conference in 2019 and wrote a blog post on; “Shortening Lithium Ion Cell Manufacturing Time: A Comparative Study of Two Methods of Making Self-Discharge Measurements.” (Click on the title to learn more!)
For a little background, lithium-Ion cells gradually discharge even when they are not connected to anything. Some self-discharge is normal. However, excess self-discharge indicates potentially catastrophic problems within the cell. Due to this, all cells are screened in manufacturing for self-discharge. There are two main methods for testing self-discharge; the delta open circuit voltage (OCV) measurement method and the potentiostatic method for measuring self-discharge current. These are illustrated in Figure 1.
Figure 1: Self-discharge measurement methods
Very briefly, for delta OCV method, the Li-Ion cell’s drop in OCV is measured over an extended period, typically days to weeks. A drop in OCV is an indirect indicator of loss of charge. In comparison, the potentiostatic method directly measures a cell’s internal self-discharge current, typically in the order of an hour. This is accomplished by holding the cell at a constant potential with a very stable external voltage source. At equilibrium, the current being supplied by the external source equals the cell’s internal self-discharge current. I have previously written about these two measurement methods in much greater detail. For reference, “Keysight Solutions for Measuring Self-Discharge of Lithium Ion Cells Achieves Revolutionary Reduction in Test Time” (click on title to access) is a worthwhile post to review to learn more about cell self-discharge and its test methods.
So now you can understand why it is a top priority for testing cells for self-discharge, and why it is vital to get consistent and valid results. This may seem straight-forward in principle. However, in practice, most find it difficult. The underlying challenge is that self-discharge is far from being fixed and constant. It is impacted by several external factors, which is what my presentation for the Novi Battery Conference was about. Top external factors affecting self-discharge measurement are illustrated in Figure 2. Note that some factors impact just the cells, just the measurement methodology, or both. This is true regardless of which measurement methodology is used.
Figure 2: External factors impacting cell self-discharge measurements
A group of sixteen 2.4 Ah 18650 size NMC cells were evaluated to assess the impact of these external factors on their self-discharge and its measurement on the two measurement methods. Let us look at a couple of these external factors in greater detail and see what the impact was.
The cell’s % State of Charge (%SoC)
The % State of Charge (SoC) has a substantial impact on the cell’s self-discharge, as shown by the blue line in the graph in Figure 3, for the cells that were evaluated in this investigation.
It was found that the self-discharge gradually fell off towards zero as the % SoC went to zero. Conversely, at the other end, it was found the self-discharge increased much more rapidly over 80% SoC. It is worth noting that internal pressure can become a factor for increased self-discharge at high SoC, due to electrode swelling.
Figure 3: Li-ion cell self-discharge versus % SoC
As can be seen, the % SoC has a substantial impact on self-discharge. Because it is affecting the actual self-discharge, it has the same impact regardless of the measurement methodology. Regardless of the actual %SoC level used, it is imperative that testing is always conducted at the same SoC level every time, to achieve consistent and valid results.
Charging and Subsequent Rest Time on the Delta OCV Method Measurement
Charging impacts the cell’s charge equilibrium which subsequently impacts the cell’s OCV decay rate characteristic, causing it to exponentially decay over time. This is shown by the orange line on the main graph in Figure 4.
Figure 4: Impact of charging and rest time on delta OCV method measurements
Charging causes a gradient of the cell’s charge that needs to redistribute itself over time to return to an equilibrium state again. The initial peak OCV decay rate depends on how heavily the cells were charged before the start of OCV measurements. The impact of charge redistribution is that it adds offset to the self-discharge measurement, until the cells are fully rested and at equilibrium again. When at equilibrium then the OCV decay rate becomes constant, due only to the cell’s self-discharge. As shown in Figure 4, for a 10-day delta OCV measurement, started after 9 days of rest, is that it adds an offset of 0.3mV, or 30% to the measurement. This is shown by the expanded view in the circle of the inserted image in the graph in Figure 4. This highlights the need for having enough rest time after charging so that the charge redistribution effect does not overwhelm the desired self-discharge measurement. Note, however, that the underlying self-discharge is unaffected. It is only the measurement method that is being impacted. The OCV drop due to self-discharge alone is still under 1 mV for the good cells and over 2 mV for the bad cells, just as for when the cells are tested when fully rested and at charge equilibrium.
Charging and Subsequent Rest Time on the Delta OCV Method Measurement
The potentiostatic method for self-discharge measurements, like the delta OCV method, is similarly impacted by charging and subsequent rest time, causing it to exponentially decay over a similar period. So regardless of measurement methodology suitable rest time is required to minimize impact of any cell charging or discharge on the measurement. Again, recent charging or discharging does not affect the actual self-discharge. It only impacts the self-discharge measurements.
Good Practices Consistently Achieve Valid Self-Discharge Measurement Results
By carefully controlling the external factors outlined, one can consistently achieve valid self-discharge measurement results. This is true regardless of using either the delta OCV method or the potentiostatic method for making self-discharge measurements. To illustrate this, both methods for self-discharge measurements were performed on the 16 cells used in this investigation. The results are plotted in the graph in Figure 5.
Figure 5: Correlating delta OCV method and potentiostatic method measurement results
The potentiostatic method measuring the self-discharge current is plotted on the vertical axis while the corresponding value using the delta OCV method measuring the rate of OCV loss for each cell is plotted on the horizontal axis. What was found is that, when a linear line was placed on all the points, they lined up with the line projecting back through the origin, demonstrating excellent correlation between the two measurement methods. Several external factors were carefully controlled between the two methods to achieve excellent correlation, including:
- Cells were charged to the same % SoC.
- Cells were fully rested to eliminate any charge redistribution effects.
- Temperature influences were minimized.
To close, self-discharge measurements are impacted by several external factors as discussed here. However, by exercising good test practices it is possible to consistently achieve valid self-discharge measurement results over time, regardless of the test methodology used.