Micro Geometry Machine Learning Optimisation

To address the pain points of time-consuming traditional LTCA and the risk of local optima, this solution integrates machine learning to build a high-precision surrogate model. Combined with advanced sampling strategies, it achieves global optimization for micro-geometry parameters. The analysis time per case drops dramatically from 6 seconds to just 0.16 ms, with prediction errors kept within 5%. This significantly shortens R&D cycles and costs, enabling rapid identification of optimal designs that balance NVH and strength, ultimately boosting the competitiveness of e-drive products.

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electric drive

NVH Transfer Path Analysis and Diagnostics

Addressing the pain points of elusive NVH sources and unquantifiable path contributions, this solution innovatively integrates TPA technology. It extends harmonic response analysis into the complex-valued phase domain to precisely decouple dynamic forces at key connections like bearings. Proven to reduce radiated sound power by 3.4 dB, it significantly boosts R&D efficiency and diagnostic accuracy, helping clients lock in optimal designs that perfectly balance quietness with strength.

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electric drive

Thermal Analysis Based on Hybrid LPTN & 2.5D FEA

Addressing the industry pain point where motor power is constrained by thermal failure, this solution innovatively employs a hybrid modeling algorithm combining LPTN with 2.5D FEA. It supports the simulation of complex cooling systems—including axial and spray cooling—striking an optimal balance between computational speed and accuracy. This significantly boosts motor thermal management development efficiency, empowering enterprises to break through power limits while ensuring safety, ultimately elevating the vehicle's dynamic performance and range competitiveness.

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electric drive