Fuel gauging for all: Page 4 of 7

August 19, 2016 // By Bakul Damle
Fuel gauging for all
As the pace of improvements in traditional consumer electronics such as smartphones and tablets is starting to plateau, many creative design engineers are focusing their attention on inventing the next big thing.

Another way to look at this is to compare the EZ vs custom tuned model at specific error budgets allowed in the system design.

Figure 5 shows how they compare with <3% error budget and <5% error budget.

Instead of simply looking at the worst case error everywhere between 0% and 100% SOC, look at the error near empty (e.g. 10%), where accurate fuel gauging really matters. If the battery is around 50% state, and the fuel gauge is indicating 40% or 60% (10% error), nothing bad is likely to happen, as no critical power management decisions are taken at that point. However, when the battery is at 10% and the fuel gauge indicates 5% SOC, then most likely, the system is going to shut down prematurely, and the battery will not be utilized fully.

On the other hand, if the battery is at 5% and the fuel gauge indicates 10% SOC, then it is likely that the system will crash unexpectedly without the benefit of a graceful planned shutdown. Both result in poor user experience – the former results in a shorter run-time than expected, while the later results in an abrupt shutdown that is very annoying for the user.

Figure 5

 

If the application has more demanding requirements, and also needs good accuracy at cold temperatures (0 degrees Celsius), then a similar analysis shows that the results are nearly the same for SOC error budget of <5%. Thus, for a large category of applications, the simplicity of implementing the EZ configuration performance becomes a game-changer for new product development.

So what allows ModelGauge m5 EZ configuration to deliver such good results?

The magic lies in how the patented ModelGauge m5 algorithm uses the real time electrical measurements and converts these into usable SOC% and other battery information. The algorithm has multiple mechanisms of desensitizing the errors due to model mismatch with the actual cells in use. These mechanisms also desensitize any errors in the electrical measurements from having adverse effects on the SOC % output. In addition, there are several adaptive mechanisms that helps the fuel gauge learn about the battery characteristics and improve its accuracy.

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