Fraunhofer HABICHT

Electrification of Powertrains

The transport sector is undergoing a transformation towards climate-friendly powertrains with significantly reduced CO₂ emission. The electrification of powertrains still remains a major challenge not only for trucks, buses, trains and ships but also for aircraft. These applications cannot be realized in the future with batteries because of the energy requirements. An extremely promising energy supplier for these applications is the fuel cell, which supplies electrical energy from stored hydrogen and ambient air. For the supply of ambient air, electrically driven turbo compressors with high speeds are required. The electric drives required for the air compressors are very demanding in terms of thermal and mechanical properties. The thermal and mechanical design shows great potential to increase power density and reliability with new material technologies. The greatest challenges arise in terms of reliability and lightweight construction. The power-to-weight ratios necessary for the aviation sector cannot be achieved with the conventional electric motor concepts from the automotive sector.

High Speed Motor for a Fuel Cell Compressor

The project aims to design and develop a high speed motor for a fuel cell compressor to enable innovation in the utility vehicle and aviation domain. The high speed motor should at least achieve a power density of 30 kW/kg by using innovative materials for the direct cooling of the stator and rotor. The rotor design will use a new manufacturing process to glue and pot the magnet in order to be suitable for high speed operation.

The design process will rely on a simulation framework. This framework will combine multiphysics simulations to consider all aspects of the thermal, mechanical and electromagnetic interactions, in the early design stage. Specific machine learning methods combined with surrogate modelling and uncertainty quantification will be employed to assist in some optimization questions and decisions during the design phase. A dedicated data space will connect the engineering knowledge, data results from the virtual design and the experimental data gained from testing and measurement combined.

Multiphysics Modelling and Smart Design Environment

Fraunhofer SCAI will contribute to the simulation framework by using its expertise in interface solutions for coupled simulation and manufacturing processes. Our MpCCI interface portfolio for co-simulation and mapping will enhance the design framework.

Fraunhofer SCAI will integrate uncertainty quantification methods in the decision-making process in order to attach a level of confidence to the simulation results.

To increase the fidelity of the virtual prototyping, Fraunhofer SCAI will adapt its Digital Twin toolbox for the modelling of material behavior as well as the electrical motor. The toolbox will deliver a set of methods to build hybrid models combining measurement and simulation data, surrogate models to reduce simulation computation effort during the optimization process, for instance. For the material design, the current VMAP standard will be adopted to exchange the information in a seamless manner among different CAE tools.

Expected Project Results

The project results will amend the hydrogen expertise within Fraunhofer by establishing a competence center for high speed electric motors. The Fraunhofer research consortium will provide a wide range of services for customers through their comprehensive and inter-disciplinary perspective which are bundled in four business units:

Integrated Systems and Device
TechnologyStructural Durability and System
ReliabilityManufacturing Technology and Advanced Materials
Algorithms and Scientific Computing

Fraunhofer SCAI will in particular enhance the multiphysics portfolio of MpCCI CouplingEnvironment in the future by including the simulation code ANSYS Maxwell. This project will demonstrate the capabilities to establish a dedicated data space enhanced by an ontology for electrical machine, and modern machine learning methods to assist the different design steps in decision-making, modelling and validation of results. A set of core competencies will be available for the future clients:

We can help the customers to automate the generated knowledge graph from the ontology based on simulation, testing and measurement data so that they can automatically link data and knowledge and use it for intelligent solutions.
We can shape the future of decision-making process driven by data and knowledge with modern machine learning technologies.
We can use multiphysics modeling to analyze the system to find potential improvements with innovative material and manufacturing process.
We can adapt data infrastructures to facilitate the data exchange during material design step as well as simulation phase to facilitate knowledge and AI access.