Correspondence:
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Corresponding author: Mohamed Essam Ahmed, AVL List GmbH, Hans List Platz 1, 8020 Graz, Austria. E-mail: mohamedessam.ahmed@avl.com
Abstract: The development of electric vehicles (EVs) presents the challenge of optimising electric machines to enhance efficiency, compactness, noise, vibration, harshness (NVH), and affordability, while reducing the dependence on heavy rare-earth materials to reduce the CO2 footprint of electric powertrains. This paper introduces a comprehensive optimization approach for the electromagnetic design of permanent magnet synchronous machines (PMSMs), employing a combination of Design of Experiment (DoE) and Robust Neural Networks (RNN). The optimization framework is utilised to comprehensively address the multiple objectives of an electric machine, including efficiency, noise, vibration, harshness (NVH), and cost. The integration of Artificial Intelligence (AI)-driven modelling has resulted in significant performance improvements, achieving up to 96% total efficiency over the entire load cycle with substantial NVH reductions of up to 20 dB, while reducing the magnet rare earth materials by 35% compared to the baseline. Furthermore, this methodology reduces the simulation time by up to 90%, demonstrating the potential of combining neural network optimization with conventional finite element simulation techniques. The validation of the AI-driven optimization approach with the measurement of the baseline and optimised electric machines for efficiency and vibration is demonstrated for correlation.
Keywords: Electric machines, electromagnetic optimization and modelling, design of experiment optimization, neural network-modelling, multi-objective and multi-physics optimization, topology optimization, robustness and sensitivity analysis, electric drive, E-machine efficiency, noise, vibration and harshness (NVH), electromagnetic finite element modelling
