View Single Post
Posts: 14 | Thanked: 3 times | Joined on Jan 2010
#20
Originally Posted by ravioli156 View Post
So you'll know that for this specific phone, 4040mAh equals 32 minutes remaining for this phone, instead of 40, because the last 10 chargings, on average, there was 32m remaining @ 4040mAh, 25m @ 4100, 15 @ 4150, 5 @ 4170.

And because a battery degrade in time, and a new firmware can also improve - or not - battery duration, your program will always be accurate.

I'm maybe wrong, but it's how I see the thing
Yes, this is precisely what I am intending. The plan is first to look at the data and inspect how variable it can be. Then to come up with a suitable algorithm/model for the prediction. That could either be rather simple (use measurements gathered at the beginning and during the charging process) or a little bit more tricky, with a learning element (prediction based not only on current charging process, but also on earlier charging processes on the local device to a certain extent). It all depends on the accuracy and variability of the data, that's why I'm trying to get as much as possible.

Once I come up with that algorithm, I can simulate the prediction for each profile that was handed in and decide whether it's enough accurate for productive use.

I believe that using some pre-computed parameter estimates based on the data from various devices within the actual program would not be very smart. (I think we agree on that point). The data is only for some early inspection, not to derive any actual numbers to be used in the program.

Matt