Accurate range prediction of E bikes with Machine Learning

MedadNewman

Just Joined
Jan 19, 2021
3
0
Hi there,

I am doing some research into E bikes and was wondering if anyone has used machine learning to do predictions of long term degradation of E bikes. I think the part that fails is the battery.
Has anyone collected long term data that includes:
  • motor current
  • batt voltage
  • state of charge
  • road conditions(inclined road, slope etc)
  • External temperature
  • E bike speed
I suspect this data would give clues on how the battery degrades. I know the Cycle Analyst V3 collects some of this data.

What do you think?

Medad
 

vfr400

Esteemed Pedelecer
Jun 12, 2011
9,822
3,986
Basildon
You'd be better off looking at Endless-sphere. I can't remember ever seeing any useful data on this forum. You'll learn a lot from the thread below:


 
Last edited:

MedadNewman

Just Joined
Jan 19, 2021
3
0
You'd be better off looking at Endless-sphere. I can't remember ever seeing any useful data on this forum. You'll learn a lot from the thread below:


Thanks for the tip
 

Nealh

Esteemed Pedelecer
Aug 7, 2014
20,110
8,219
60
West Sx RH
The best unbiased resource is the ES cell aging thread, the guys over there go to great lengths and post stuff that you simply don' see elsewhere.
 

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