So how well does this work in practice? Being very mindful of Edison’s observation in 1883: “Just as soon as a man gets working on the secondary battery it brings out his latent capacity for lying," it is important to be very thorough and clear about the performance of this new technology, so as not to overstate the facts.
Maxim has developed a vast battery database consisting of cell characteristics and behavior over a variety of test conditions similar to the customers’ use cases. This allows Maxim to validate any new improvements in the fuel gauge algorithm, by running it on the real data collected previously. Using this data, Maxim analyzed the performance over hundreds of batteries of various sizes and plotted a histogram of the results in figure 3.
This shows that more than 94% of test cases at room temperature and above have less than 3% SOC error. These test cases do exclude certain battery types that are known to be quite different in terms of open-circuit voltage (OCV) vs SOC% table, compared to the more conventional and popular chemistries.
While these results look very good, how much are we giving up in terms of performance if we used a custom-tuned battery model in each case?
Figure 4 is a histogram that shows a comparison of the EZ model vs a “tuned” custom model, plotted as percentile of test cases vs the error bucket they fall into. While the tuned model indeed places a higher number of cases in the 1% bucket, the aggregate of all test cases up to 3% error shows that the EZ model covers 95% while the custom mode covers 97% of the test cases. Considering the extra effort, resources, and time required to prepare a custom model, the EZ model starts looking very attractive indeed.