TREMEC – Machine learning from TensorFlow to Micro-Controller Unit

Contact:        Jürgen Lambrecht – jurgen.lambrecht@tremec.com
Registratie:  De student kan zich aanmelden via jobs.be@tremec.com t.a.v. Yana Feyers.

Probleemstelling

Machine Learning component for the motor control software of an electrical car.

The latest generation of automotive micro-controller units (MCU) contain hardware blocks to run machine learning (ML) algorithms. At Tremec we intend to use such MCU’s in our inverters, which together with the electric motor form the most important part of the electric drive unit (EDU) in electric vehicles (EV). In high performance EV applications, the traction inverter is the core element that allows a particular motor to shine and deliver its maximum performance. High speed Space Vector PWM (SVPWM), Field Oriented Control (FOC) together with an angle tracking observer (ATO) are the main methods of the motor control software (SW) to realize this. But methods to reduce the noise of the in- and output signals are maybe even more important. With this thesis, we want to learn where ML can be useful for motor control SW and gain practical knowledge on how to implement such models in an automotive MCU.

Doelstellingen

The goal is twofold, the first one high-level and the second one embedded:

• Many papers refer on using Machine Learning (ML) techniques to replace some parts of the inverter software. Select or create such a model.
• The new Infineon MCU (TC4xx) contains DSPs that can be used for AI: implement a TensorFlow model on it.

The main result is a working inverter software where a part is replaced by a ML implementation.

But also: Literature study of existing ML models, 1 or more trained ML models, a clear how-to document.