Electric vehicles (EVs) face unique NVH challenges due to the absence of engine masking effects. Gear whine in reducers becomes particularly noticeable, originating from transmission error (TE) during gear meshing. This high-frequency noise occurs at the gear meshing frequency:
$$ f = \frac{N \times n}{60} $$
where \( f \) represents meshing frequency (Hz), \( N \) is tooth count, and \( n \) denotes rotational speed (rpm). For a typical EV motor spinning at 15,000 rpm driving a 14-tooth electric vehicle gear, the meshing frequency reaches 3,500 Hz – directly exciting multiple reducer housing modes (lowest at 450 Hz). Since modal avoidance is impractical, TE reduction becomes critical for NVH refinement.
Transmission Error Fundamentals
Transmission error quantifies positional deviation between theoretical and actual gear rotation. For perfectly rigid electric vehicle gears with zero manufacturing/assembly errors, kinematic consistency requires:
$$ \theta_1 \times r_{b1} = \theta_2 \times r_{b2} $$
where \( \theta \) represents angular displacement and \( r_b \) base circle radius. Real-world deviations cause TE, expressed linearly as:
$$ TE = \left| \theta_2 \times r_{b2} – \theta_1 \times r_{b1} \right| $$
Key factors influencing TE in electric vehicle gears include manufacturing tolerances, assembly misalignment, tooth stiffness variations, and load-induced deformations. Excessive TE excites structural resonances, generating audible whine.
Case Study: TE Analysis of EV Reduction Gear
We analyzed a first-stage helical gear pair from a production EV reducer using KISSSOFT. Parameters and operating conditions appear in Tables 1 and 2.
| Parameter | Pinion | Gear |
|---|---|---|
| Teeth | 14 | 58 |
| Module (mm) | 3.2 | 3.2 |
| Pressure Angle | 20° | 20° |
| Helix Angle | 23° | 23° |
| Face Width (mm) | 34 | 32 |
| Total Contact Ratio | 3.103 | |
| Input Torque | 240 Nm |
| Input Speed | 4,500 rpm |
| Power | 113 kW |
Contact analysis revealed excessive peak-to-peak TE of 2.4976 μm (Figure 1), significantly higher than the 1 μm NVH target. Additionally, the load distribution showed stress concentration at tooth edges with peak unit loading reaching 1,741.6 N/mm².
TE Optimization Through Electric Vehicle Gear Modification
To address these issues, we implemented profile and lead modifications:
- Lead Modification: Combined linear taper (10 μm at both ends) with crowning (5 μm amplitude). Total lead deviation: -5 μm (left end) to -15 μm (right end)
- Profile Modification: 5 μm tip relief applied symmetrically
The optimization achieved remarkable improvements:
| Parameter | Original | Optimized | Improvement |
|---|---|---|---|
| Peak TE (μm) | 2.4976 | 0.4932 | 80.2% |
| Peak Unit Load (N/mm²) | 1741.6 | 1269.5 | 27.1% |
| Load Distribution | Edge-loaded | Uniform | – |
Post-modification TE measurements (Figure 2) confirm stable meshing behavior. The load distribution became uniform across the tooth surface, significantly reducing bending stress and contact pressure. This electric vehicle gear optimization demonstrates that targeted micro-geometry adjustments can yield substantial NVH gains without structural changes.
Conclusion
Transmission error directly drives gear whine in electric vehicle reducers. This study established a systematic approach for electric vehicle gear optimization: 1) Quantify baseline TE using specialized software, 2) Identify load distribution flaws, 3) Apply calculated profile/lead modifications. The 80% TE reduction achieved validates gear microgeometry refinement as a highly effective solution for EV whine suppression. Future work will extend this methodology to multi-stage reducers under dynamic loading conditions.