Cycling Power Estimator
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@mickywickyftw I owe you a feedback on previous changes (not the 13th July one): far better in descents. They are still some spikes (above 450W), when the road suddenly changes (from negative to positive grade for example). I still used 3s for the power average. I’ll switch to 10s for next moves.
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@mickywickyftw unfortunately, there are regressions in the last update. My feeling is that power is overestimated in descents (and is now almost as high as power in ascents). Just my feeling, as I do not have a real power meter to validate this. I would say that power should somehow be related to heart rate.
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The power estimator calculates the mechanical force required to move at a given speed on a given slope (with weight, terrain, etc. taken into account).
Heart rate correlates with power over longer efforts but diverges significantly in short bursts (because it reacts more slowly) on descents (because you don’t have to push as hard to produce the same acceleration), and in heat (for obvious reasons). The power model doesn’t see fatigue, cardiac drift, or how hard your legs feel. But that is not its purpose either.The spikes are a known issue caused by the grade estimation model. They happen at grade transitions, e.g. when the road flips from downhill to uphill. Grade snaps from the descent average to the ascent average in a single second, simultaneously with the acceleration that naturally occurs there. That combination can briefly produce very high values. Difficult to work around for now, and given the altimeter’s 20cm granularity, it may not be achievable, but I’m still looking.
Descent overestimation: I thought this might be the case. Without a cadence sensor the model has no way to detect coasting, since it cannot tell whether you’re pedalling or freewheeling. So it has to assume by comparing your speed to terminal velocity, which is calculated using… the grade estimation. So we’re back to that limitation.
On the topic of the grade estimation used by Suunto for NGP, that algo seems to suffer the same issues.
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@mickywickyftw I’m aware that the model has some limitations (especially due to grade variations and without cadence sensor), but getting the same power estimation (~ 220/250W) in both a 10 minutes descent and ascent is quite unexpected (at 100bpm and 140bpm, respectively). Same position on bike. As far as I remember, I did not observe this on previous versions (I switched to 10s window today though, so not exactly same settings).
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@bubuche 200-250W is very high for a descent, true. Too high for the model as it should be I think.
Can you help narrow it down, as I have not observed this myself?
Zoom in on the downhill section, and note:- % grade
- distance
- start and end elevation
- Avg power
- Avg speed
- Your total weight as configured in the settings (bike + rider + pack)
If you can zoom to just that section, overlay speed and power, that might give us a clue.
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I think I’ve worked it out - it was a typo! Please try the latest build.
It meant that terminal velocity was ignored when going downhill without a sensor - resulting in those high power readings.
Let me know, as I cannot test this for a couple of weeks now. -
I think I’ve worked it out - it was a typo!
Ok, I’ll try in a few days
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I think I’ve worked it out - it was a typo!
Changing a + to à - helped a lot
This is far better in descents.In a previous post, you requested the avg speed and power on a section. How? I can only get this for segments of 1/5/10 km.
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@bubuche I would use Runalyze or another external platform for that.
Now I‘ve been using the latest updates for a bit, I think we‘re there.
I thought about using predicted wind from the weather app, but you end up losing accuracy as soon as buildings are involved.
I will look at using the accelerometer perhaps, but felt it was overkill when I started with this app. It might solve the grade latency issue though. I‘ll play around with it at some point.
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