Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches

Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a holistic, front-loaded approach to NVH optimisation, leveraging simulation models, machine learning, and target cascading to deliver superior results. By attacking NVH at the source, and integrating data-driven techniques early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning algorithms are utilised to accelerate root cause analysis and refine simulation accuracy, while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, setting the benchmark for next-generation electric powertrain development. several application and real life exmaple are shown.