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Background <p>In electric vehicle (EV) drive assemblies, macro-geometric parameters govern internal parametric excitations, directly influencing high-frequency nonlinear dynamics. While controlling dynamic transmission error (DTE) under ideal conditions is a primary design objective, the nonlinear amplification of geometric parameter variations within a fully coupled system remains insufficiently quantified. A systematic parametric robustness assessment connecting individual macro-geometric parameters to DTE and dynamic stability has been lacking. To address this gap, this study presents a systematic parametric robustness assessment that quantifies the nonlinear sensitivity of DTE to selected discrete macro-geometric configurations.</p> Methods <p>This assessment is conducted using a 17-degree-of-freedom (DOF) nonlinear dynamic model that incorporates translational-torsional coupling effects. The core excitation source, time-varying mesh stiffness (TVMS), is calculated via the potential energy method (PEM), and the differential equations of motion are solved using ode15s. The analysis is supported by nonlinear dynamics tools, including spectrum plots, phase diagrams, Poincaré sections, and bifurcation diagrams. Parametric simulations assess system sensitivity by evaluating representative discrete configurations around the nominal design baseline. The investigated variables focus on key macro-geometric parameters: helix angle, module, addendum coefficient, bore diameter, and face width. </p> Results <p>Numerical results indicate that alterations in these geometric configurations can induce substantial changes in DTE. The system inherently amplifies these variations non-linearly, capable of transforming its dynamic behavior from stable periodic motion to a complex chaotic state. </p> Conclusions <p>These findings support a physics-based interpretation of how selected macro-geometric parameters influence system dynamic stability within the investigated design space. This work provides conditional theoretical and engineering insights for DTE-oriented robust design of EV drive assemblies. </p>

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Nonlinear Dynamic Analysis of Electric Vehicle Drive Assemblies: a Parametric Robustness Assessment of Macro-Geometric Configurations

  • 期刊论文

  • 发表日期：

    2026年7月2日

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  • Yuhang Hu,
  • Yu Liu,
  • Zonghong Wei,
  • Wenchao Gao,
  • Yong Chen

摘要

Background

In electric vehicle (EV) drive assemblies, macro-geometric parameters govern internal parametric excitations, directly influencing high-frequency nonlinear dynamics. While controlling dynamic transmission error (DTE) under ideal conditions is a primary design objective, the nonlinear amplification of geometric parameter variations within a fully coupled system remains insufficiently quantified. A systematic parametric robustness assessment connecting individual macro-geometric parameters to DTE and dynamic stability has been lacking. To address this gap, this study presents a systematic parametric robustness assessment that quantifies the nonlinear sensitivity of DTE to selected discrete macro-geometric configurations.

Methods

This assessment is conducted using a 17-degree-of-freedom (DOF) nonlinear dynamic model that incorporates translational-torsional coupling effects. The core excitation source, time-varying mesh stiffness (TVMS), is calculated via the potential energy method (PEM), and the differential equations of motion are solved using ode15s. The analysis is supported by nonlinear dynamics tools, including spectrum plots, phase diagrams, Poincaré sections, and bifurcation diagrams. Parametric simulations assess system sensitivity by evaluating representative discrete configurations around the nominal design baseline. The investigated variables focus on key macro-geometric parameters: helix angle, module, addendum coefficient, bore diameter, and face width.

Results

Numerical results indicate that alterations in these geometric configurations can induce substantial changes in DTE. The system inherently amplifies these variations non-linearly, capable of transforming its dynamic behavior from stable periodic motion to a complex chaotic state.

Conclusions

These findings support a physics-based interpretation of how selected macro-geometric parameters influence system dynamic stability within the investigated design space. This work provides conditional theoretical and engineering insights for DTE-oriented robust design of EV drive assemblies.