Ghost Orders and Gear Tooth Surface Waviness in EV Drivetrains

Electric vehicle drivetrains reveal tonal gearbox noise once masked by combustion engines. Among the most persistent are ghost orders narrowband spectral components not aligned with nominal mesh harmonics linked to periodic gear tooth surface waviness. This review synthesizes research on the w aviness to transmission error to ghost order pathway focusing on detection modeling and validation methods relevant to EVs. Measurement techniques range from single flank transmission error tests and torsional order tracking to high speed full flank metrology with advanced waviness analysis enabling earlier identification of tonal risk. Modeling approaches include quasi static loaded tooth contact analysis with sinusoidal superposition multi body dynamics with micro geometry driven transmission error modulation and hybrid finite element multi body dynamics workflows that integrate measured topography. Findings show that circumferentially coherent waviness even at sub micron amplitudes can produce audible ghost tones in low damping EV drivetrains especially when coinciding with structural resonances. However predictive accuracy remains limited by inconsistent waviness terminology incomplete data transfer from metrology to simulation and scarce EV representative validation under varying loads and speeds. The emerging trend is toward integrated design manufacturing simulations where measured flank surface data directly informs noise prediction models. Standardized waviness metrics routine use of measured topographies in contact models and psychoacoustic or machine learning aided tonal assessment are identified as priorities for improving ghost order prediction and mitigation in EV gearbox design and production flow.