A use case for average voltage vs capacity.
Suppose you're supporting a team that is designing a BMS that is used to monitor your company's cells. The BMS needs to accurately monitor cell voltages to estimate useful metrics like state of health (SOH). They propose building a look-up table that maps measured voltage during discharge to a capacity.
You receive a request to send them the data necessary for them to produce this look-up table. Many companies have existing test data that can fulfill this request. Two typical plots used to answer this question are a capacity (%) vs. cycles plot, and a voltage vs. cycles plot.
However, a problem quickly becomes apparent. Real data is often noisy and is obtained under non-laboratory conditions. For example, at 1500 cycles, the capacity is 90% of initial capacity. What should be the corresponding voltage that is programmed into the BMS? What if the BMS needs resolution between 89% of initial capacity, or 91% of initial capacity?
One possible solution to this problem is to plot Average Voltage on the Y axis, and Capacity on the X axis. This removes cycle numbers from the plot, which doesn't add meaningful value to the analysis.
From the plot of Discharge Voltage vs Capacity, the range of possible average voltages is now obvious for each capacity. At 90% of initial capacity, the range of expected voltages ranges from 3.71V to 3.73V. A noisy capacity & voltage vs. cycles plot has been distilled down to only the essential information, as well as the measured variation.
Did you know that you can have 2 plots side-by-side in Voltaiq, for easy comparison and analysis? If you were tasked with populating a BMS's look-up table with capacity and voltage values, which plot would you prefer?
Try plotting it yourself:
1. Load your data into a workspace
2. Choose 'Discharge Capacity' for your X axis
3. Choose 'Mean Discharge Potential (Capacity-weighted)' for your Y axis
4. Go to Formatting in the top right, and under 'Bulk Trace Options', choose 'Marker Only'