Problem

A leading automotive OEM approached Secondmind seeking to calibrate a Permanent Magnet Synchronous Motor (PMSM), with the goal of producing optimized calibration maps by minimizing the current amplitude for a target Torque or maximizing the efficiency at all speed and load conditions.

The influence of the rotor temperature is of particular interest, as it affects the strength of the motor’s magnet, so a wide range of measurements is required. However, as measurements are taken, the rotor temperature rises and the calibration engineer must wait for it to cool sufficiently before the next cycle can begin.

Solution

Secondmind worked with the OEM to create a solution that:

• Uses an intelligent temperature control algorithm to minimise cooling times during data measurements.
• Combines physics-informed models with machine learning models, leading to highly accurate models.
• Avoids major transient effects, allowing faster data measurements.
• Enables multi-parameter optimization: Motor speed, DC voltages, Current amplitude, Current phase angle, Rotor Temperature and DC current.

Impact

• Reduced the time to generate calibration maps from hours to minutes to minimize test bed usage.
• Produced higher precision calibration maps to maximise the efficiency of the e-motor and inverter to increase the range of the electric vehicle.