The scope of this master thesis is:
- Develop a physics-informed neural network for junction and coolant outlet temperature prediction in an automotive inverter under varying electrical and cooling conditions
- Based on the thermal governing equation introduced earlier.
- If necessary, extend the model to include additional heat-transfer paths, such as ambient convection and thermal coupling effects.
- Estimate power loss and thermal resistance from available reference data using AI modelling.
- Ensure real-time model speed capability.
- Define representative driving profiles and measurement conditions for testbench data acquisition for model training
- Select operating points that cover relevant electrical and thermal conditions.
- Ensure that the collected data are suitable for model training and validation.
- Develop a scripted workflow for automated training and deployment
- Automated model training.
- Configurable network architecture, layer sizes, node numbers, and training strategy.
- Automatic export of the corresponding Simulink model.
Bearbeiter: Krithi Nagarajan
Betreuer: Dragan Stojkovic, Dr. Stefan Weigl (Schaeffler)
Verantwortlicher: Prof. Dr.-Ing. Martin März