EngD on identification of ecological design properties for electrical machines

Demand for high performance and high speed applications (hybrid/electrical vehicles, airborne applications, compressors, etc.) are rapidly increasing in the global markets due to emerging electrification and substantiality issues. Many companies are looking for innovations to improve power density, cost and efficiency of the electric motors, which is an important component in such high performance applications.

In electric motors (EMs), stator and rotor iron/e-stack design play an important role for the performance of the EM. One of the development measures to improve the costs of the electric motor is eliminating or reducing permanent magnets. Common examples of such technology are electrically excited synchronous machines (ESM), (magnet assisted) synchronous reluctance machines (SynRM). Yet, the market is still expected to be driven by permanent magnet synchronous machines (PSM) as well. Another practice is also combining these machine technologies into 'hybrid machines' to combine several advantages.

The target of this project is to find sustainable design and material solutions to optimize performance and cost of ESM, PSM, (magnet assisted) SynRM and hybrid machine e-stacks (rotor and stator) considering the company's production and process capabilities.

EngD Task description and Deliverables
Defining design requirements and bottlenecks of ESM, PSM, (magnet assisted) SynRM and hybrid machines for the target markets prioritized by the company
Brainstorming design, materials (iron stack (Si, Ni, Co, etc…)) and production solutions by aligning with the production team. Investigating possible benefits of the solutions (e.g. simulations, measurements)
Evaluating the current E-stack production process for all the active materials, focusing on the added value of their recyclability and eco-design for circularity/sustainability,
Creating a proposal for required process changes towards eco-design of E-stacks and assisting in the development of AI based material models for the new process
Defining opportunities and risks as a team member in internal activities