Turbo Vado 2 4.0 or 5.0 or 6.0?

[Jeremy McCreary]

A new trial, a little longer and with more rider effort to more clearly define Pm saturation values. I just did 40/40 and 40/100.

We clearly have the right guy on the field work. Thanks!

Q: Just to be careful, your cadences on these trials?

.fit file data
View attachment 206039

View attachment 206040

Again, strong confirmation that Pm really is linear in Pr before saturation and almost flat in Pr after. So that part of my simple Pm model holds fairly well. And that makes our reverse-engineering task much easier.

and my comparison plotting the functions together:
View attachment 206041

Excellent! Writing an assist mode as E/M, let
Pe = electrical motor power
Prs = rider power Pr at the saturation point where ramp meets flat
Pes = Pe at Prs
Pex = Pes at M = 100
S = ramp slope.

Took the S values below from the .FIT file linear regressions and visually estimated the Prs and Pes values from your summary graph.

For Ride 1 at 40/40, Prs1 ≈ 285W, Pes1 ≈ 210W, and S1 = 0.7

For Ride 2 at 40/100, Prs2 ≈ 245W, Pes2 = Pex ≈ 380W, and S2 = 1.51.

In principle, we should get S1 ≈ Pes1 / Prs1 = 210 / 285 = 0.74 and S2 ≈ Pes2 / Prs2 = 380 / 245 = 1.55. And in both cases, we get reasonable agreement. So far, so good!

But the empirical Pes values raise an issue. My simple Pm model uses

Pes = Pex (M / 100)

If that were true, we should have Pes1 / Pes2 = M1 / M2 = 40 / 100 = 0.40. But empirically, we get Pes1 / Pes2 = 210 / 380 = 0.55, a significant discrepancy.

So Pes must be more complicated than the simple model assumes. Efficiency variation via cadence could be involved.

Problem is, if you can't predict Pes from just M and Pex, it's gonna be hard to estimate Prs — which is my main goal in this reverse-engineering project.