Yamaha VX Cruiser HO (2023) with AI generated RPM and fuel usage data

Описание: Using AI to Estimate RPM and Fuel Usage from Speed Data with Python.

My script uses interpolation techniques to estimate engine RPM and fuel consumption (litres per hour) based on GPS speed data. It’s designed for Yamaha VX Cruiser HO but can be adapted for other Jet Skis.

We use real-world RPM vs. speed benchmarks and fuel usage curves, then apply cubic interpolation to augment .gpx, .csv, or .fit ride data with estimated engine load.

Speed in MPH (converted from 0, 8, 15, 25, 35, 45, 50, 58, 64, 70, 75, 85, 90+ km/h)
speed_data_mph = np.array([0, 5, 9, 15, 25, 35, 45, 50, 58, 64, 70, 75, 80]) # mph

Matched RPM values based on real-world VX Cruiser HO behavior
rpm_data = np.array([
1300, # Idle
1500, # No wake mode
2500, # Max no wake
3500, # Pre-planing
4500, # 25 km/h cruise
5500, # 35 km/h
6200, # 45 km/h
6500, # 50 km/h
7000, # 58 km/h
7500, # 64 km/h
7600, # 70 km/h
7700, # 75 km/h
7800 # Full throttle
], dtype=float)

Data points: [RPM, Fuel Consumption in Liters per Hour (LPH)]
fuel_rpm_data = np.array([1500, 3000, 4500, 5500, 6500, 7500, 7800])
fuel_lph_data = np.array([1.9, 7.5, 15, 25, 34, 55, 60])

Let me know if this is correct or needs some tweaking.
It's based on a rider weight of 75kgs and calm conditions.

https://github.com/RPeterJ/PWC-Teleme...