relationships between design change and change in behaviour using traditional design methods. Multi-disciplinary and multiphysics optimisation methodologies make it possible to design an e-motor for multiple, completely different design requirements simultaneously, thus avoiding a serial development strategy, where a larger number of design iterations are necessary to fulfil all requirements and unfavourable design compromises need to be accepted. Multiphysics and multi-disciplinary optimisation, however, need efficient processes to be executed within the narrow constraints and time limitations of a live product development. The processes need to be integrated with all departments in the e-motor development.

PORSCHE’S OPTIMISED ALTAIR ENVIRONMENT A baseline design is used as a starting point for the optimisation. A design space is then created by defining variables (design variables, DVs) that influence the design. In this study shape variables, which influence the size and position of the magnets, are used to create the design space. Then, the essential responses are selected. Depending on the choice of responses, one or more solvers must be used to perform one or more simulations to yield the necessary responses. In certain cases, co-simulation of solvers is necessary to resolve responses depending on each other, i.e. multiphysics situations, such as where situations with time- dependent output from one solver is needed to solve responses in another solver and vice versa. Before launching the study, general study parameters must be defined and how the optimisation should be executed. If metamodel-based optimsation is chosen, response surfaces of all responses are created based on the samples from the DoE (Design of Experiments). Optimisation and design exploration can then be carried out using these response surfaces. A strength of DoE-based optimisation is clearly the ability to use the data to answer a large number of different questions and to play through numerous design scenarios. Porsche is developing high-

performance e-motors with high requirements on key performance data such as power, torque and speed.

Temperature profile after 2 hours

Porsche and Altair agreed on applying a three-step, optimisation-driven design process to develop a concept. The first phase supports the

development of a baseline combination of stator and rotor concepts focusing on magnet configuration. For each magnet configuration, design optimisation is executed to derive an optimal set of design parameters.

In the second phase, the design scope

is extended to include other important physics to be considered during the e-motor design process. In addition to phase one, heat transfer, structural strength and demagnetisation responses are added to the design problem. The third phase is focused on looking

at the e-motor in its environment and thus including other parts of the drivetrain. As a first step, the inverter is added to generate more realistic currents into the e-motor design process. Later, the systems approach will be used to calculate efficiencies and temperature development for complete drive cycles.

THE DETAILS The first phase concerns the task of finding the right starting point for the multiphysics design process. Altair’s FluxMotor was chosen for this task. Based on a classical rotor topology, different winding configurations were investigated with respect to maximum torque and power for one working point close to the base point. When the preferred winding configuration has

been found, the next task is to find the best matching rotor configuration based on the design requirements stated for the e-motor to be developed. In this project, four competing rotor designs were investigated and compared. In FluxMotor different test scenarios are available to analyse a motor concept and for the requirements, the ‘efficiency map test’ was chosen to compare the four different topologies. From this test, both the base point and the max point data could be extracted and used for the comparison of the designs. Four different topologies were tested. To satisfy different requirements coming from different physics, a strategy was chosen to use a multi-disciplinary optimsation process, in which several computations using different tools were used. The different tools and simulations where necessary to calculate all requested responses for the multiphysics design optimisation problem. The simulation types and the working points were chosen such that all responses could be extracted using a minimum of calculation effort. To study the mentioned working

points, FluxMotor was first used to extract the main characteristics of the motor, such as speed, current rms and control angle values. These values being known, FE-based tools could be used to accurately calculate all motor characteristics including iron losses, efficiency, etc. Flux 2D was the tool used to completely derive the e-motor 27

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