Another new TQ motor: hpr40

[stompandgo]

Some of us also have real world, on-road metrics and data from our rides and races to go by. There's also the unmeasurable RPE (Relative Perceived Exertion) data that is accurate only for that rider.

The developer contacted me and explained his methodology and the systematic approach needed to get an accurate outcome. I was pleased that he does not claim it to be gospel, only an estimate. I will go through the motions and follow his instructions for my last long ride, so that we can see how close he came to predicting the eventual usage. The one variable that I use on almost every ride is the effect of shutting Off. I may turn off assist for large percentages of the ride, and there's no realistic way to predict how much Off time there would be because it is so dependent on RPE, terrain, road conditions, atmospheric conditions, etc.

I am very intrigued by this model, however. I have a ride I plan to do soon that, even with the range extender and significant time in Off, may run out of assist. I'd like to see what it comes up with.

 

[Jeremy McCreary]

Some of us also have real world, on-road metrics and data from our rides and races to go by.

Would love to have that kind of experience to draw on — no doubt another very useful lens on your riding. I say, the more lenses, the better.

The developer contacted me and explained his methodology and the systematic approach needed to get an accurate outcome. I was pleased that he does not claim it to be gospel, only an estimate. I will go through the motions and follow his instructions for my last long ride, so that we can see how close he came to predicting the eventual usage. The one variable that I use on almost every ride is the effect of shutting Off. I may turn off assist for large percentages of the ride, and there's no realistic way to predict how much Off time there would be because it is so dependent on RPE, terrain, road conditions, atmospheric conditions, etc.

I rarely stick to a planned route, much less a script on how to ride it. Hard to deal with that in any conceivable pre-ride range prediction scheme.

But if you find the app reasonably accurate when you actually ride a route the way you told it you would, you could use a prediction as a reference case for range management en route.

"I have the battery to ride this route this way with roughly this much left over." Your thinking about range could then delta off that case as the ride evolves.

For example, if the app predicts 20% battery left for a particular route and riding script, you should probably either (a) avoid battery-hungry deviations, or (b) compensate with some battery-saving deviations elsewhere.

The predictions might also help you decide when it's safe to leave that heavy range extender at home — with the understanding, of course, that the RE comes along in borderline cases.