Multi-Objective Optimisation Framework for Electrical Machines based on Open Source Platforms

Multi-Objective Optimisation Framework for Electrical Machines based on Open Source Platforms

Rajdev, Ansh (TU Delft Electrical Engineering, Mathematics and Computer Science)

Dong, J. (mentor)
Ragni, D. (mentor)
Bauer, P. (graduation committee)

Delft University of Technology

Electrical Engineering

2022-08-25

The design of electrical machines is complicated due to the number of variables involved and due to competing objectives like efficiency, weight and cost. Another important aspect is the involvement of different physical phenomena such as torque production, electromagnetic fields and thermal heat flow. Thus designs need to satisfy multiple constraints and fulfill competing objectives making the design process tedious. Multi-Objective Optimisation algorithms provide a set of designs that are Pareto optimal i.e. any improvement in one objective comes at the cost of performance in another objective. This gives the designer a set of designs with different trade-offs between objectives and they can choose the design that satisfies the objectives and constraints the best.

There are numerous commercial software available that provide these functionalities. However the methods used to model machines within these packages are not available or cannot be changed. They are also usually expensive. This makes the exploration of new limits and new topologies difficult. Using open source packages allows us to modify these methods according to our application and gives greater control over the process.

This thesis uses PYLEECAN python library that is based on FEMM software to perform the analysis of the machine. A six time-step magnetostatic analysis method to calculate the average torque and iron losses in the stator and rotor core is presented along with methods to calculate the copper losses and windage losses. A steady state Lumped Parameter Thermal Network (LPTN) is developed to calculate the temperatures of various parts of the machine. The LPTN is capable of estimating the temperatures under natural convection and forced air cooling conditions.
Finally a MOO framework was developed using the models developed and the PYMOO python library. This thesis uses NSGA-II to perform the MOO. The MOO framework was used to optimise the design of a machine for a drone application and explore the specific power density limit of the machine. The power density limit was found to 5 − 7 kW/kg based on different slot pole combinations, winding temperature limits and core material used. Further, insights into how different machine parameters affect the specific power density are presented

Multi Objective Optimisation
Permanent Magnet Machine
Drone

http://resolver.tudelft.nl/uuid:2bc07fcb-2797-4236-b076-039da4994807

Student theses

master thesis

© 2022 Ansh Rajdev