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Tesla’s motor guru says simulations are key to avoiding exotic materials and expensive manufacturing methods

Tesla Model S Motor Windell Oskay copy

It comes as no surprise that Tesla, the EV trailblazer, spends a considerable amount of resources on internal R&D to develop better parts for EVs, and that its testing facilities and engineering talent are at the forefront of the industry.

Charged recently chatted with Tesla’s Principal Motor Designer, Konstantinos Laskaris, to learn more about the process the company uses to continually evaluate and optimize motor design choices.

“At our factory in Fremont, we manufacture practically every aspect of the car in-house,” said Laskaris. “We have a motor winding and manufacturing facility, so we can optimize every aspect of our motor manufacturing and control the quality of the product. Also, we can implement changes in production very fast – we’re a very agile company from that perspective.”

Laskaris has a background in creating the algorithms that allow computers to simulate how a motor will function in the real world, and he explained how these simulations translate into better vehicles:

The mathematical modeling technology, or methodology, that you use is very important and has tremendous impact on the success of electric vehicles. When I say “modeling,” I mean to understand the mathematical principles behind a system and then create software tools that will represent accurately how it will act in the real word.

Accurate motor modeling is so important because through it we can evaluate a hypothetical motor before we produce it – the losses, performance capabilities, torque ripple, thermal management, and anything that we are interested in to classify how good or bad is a motor. And, in this way, we avoid unnecessary prototypes and unpleasant surprises.

Beyond that, through good motor modeling, we can achieve the best optimization – which means we can achieve exotic performance without the use of exotic materials and exotic manufacturing methods.

Optimization is the art of being able to navigate through different motor candidates to see what is good and what is bad, and by how much. As you begin to do optimization, you realize that without good modeling, it is meaningless. This is because the process would be based on a bad representation of the motor, and in the end the motor wouldn’t be truly optimal.

We can generate motor geometries and analyze them with finite element analysis very quickly. We have a big computer cluster with more than 500 core processors that run finite elements. That means you can create many virtual models in parallel and do a very large number of calculations. Basically it allows us to solve the loss and efficiency maps very fast and see – according to any metrics that we created – how good any motor design is for an application we’re designing for.


The full interview with Konstantinos Laskaris is available in Charged – Issue 21.
Image: Windell Oskay (CC BY 2.0)

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