We evaluated a large amount of data sets across many scenarios of the automotive ecosystem:
Electric Motor and Vehicle
- Electric Motor parameters
- Smart-Grid Smart-City Electric Vehicle Trial Data
- Torque Characteristics of a Permanent Magnet Motor
Shared Mobility
- Shared Car Locations City of Tel Aviv
- Car Sharing Vancouver
- Car Sharing Turin
- Transportation Network Providers – Trips
Heartbeat Signals
- PPG Heartbeat for Cognitive Fatigue Prediction
- ECG Heartbeat Categorized Dataset
- Biometric for Stress Monitoring
Traffic and Accident
- LSTW: Large-Scale Traffic and Weather Events Dataset
– Travel time estimation
– Traffic prediction - US Country Wide Traffic Accident prediction Dataset
Case study
Electric Motors - virtual sensors
Application of several ML models algorithms to get strong estimators for the rotor temperature and torque of the permanent magnet synchronous motor (PMSM).
Impact: help the automotive industry manufacturers in being more effective in the production, by enabling control strategies to use the motor to its maximum capability.
The result have been an high prediction accuracy, along with a high confidence in preemptive issues detection:
