In the rapidly evolving landscape of new energy vehicles, the emphasis on lightweight and efficient design has become paramount. As electric vehicle adoption grows, addressing issues such as excessive mass, structural complexity, and low material utilization in mechanical components is critical for enhancing performance and energy efficiency. This study focuses on the optimization of a rear drive axle housing for a China EV model, leveraging advanced simulation techniques to achieve significant weight reduction while maintaining structural integrity. The drive axle housing, a key component in the transmission system, is subjected to rigorous analysis to ensure it meets the demands of modern electric vehicle applications. Through this work, we aim to contribute to the broader goals of improving the sustainability and performance of electric vehicles in China and beyond.
The drive axle housing in an electric vehicle plays a crucial role in transmitting power from the motor to the wheels, and its design directly impacts overall vehicle efficiency. Traditional designs often lead to unnecessary weight, which can reduce the range and increase energy consumption—a significant concern for electric vehicles. In this analysis, we begin by creating a detailed three-dimensional model of the drive axle housing using SolidWorks software. The model is simplified to focus on essential features, reducing computational overhead for subsequent finite element analysis. This step is vital for accurately representing the structure and ensuring that optimization efforts are based on realistic geometries. The adoption of such digital tools is increasingly common in the development of China EV components, enabling faster iterations and more precise designs.
Following the 3D modeling, we proceed to establish a finite element model in ANSYS Workbench. The material selected for the drive axle housing is 45 steel, with defined properties including an elastic modulus of 210 GPa, a Poisson’s ratio of 0.3, and a yield strength of 355 MPa. Mesh generation is a critical aspect of this process; we employ a hybrid approach that uses finer elements in high-stress regions and coarser elements in less critical areas. This ensures computational efficiency without sacrificing accuracy. The resulting mesh consists of approximately 817,969 nodes and 471,757 elements, with an average mesh quality of 0.773, meeting the requirements for reliable finite element simulations. This methodology is particularly relevant for electric vehicle components, where weight savings must not compromise durability or safety.
To evaluate the structural performance under various operating conditions, we conduct a static analysis considering four representative scenarios: maximum vertical load, maximum traction, maximum braking, and maximum lateral force. For each case, we calculate the applied forces and torques using established formulas. For instance, the maximum vertical force on one side of the spring seat under full load and dynamic conditions is given by:
$$F_1 = \frac{m \cdot k_d \cdot g}{2}$$
where m is the mass under full load, k_d is the dynamic load coefficient, and g is the acceleration due to gravity. Similarly, the torque during acceleration is derived from:
$$T_2 = \frac{T_{\text{max}} \cdot i \cdot \eta}{2}$$
with T_max representing the motor’s maximum output torque, i the transmission ratio, and η the transmission efficiency. The results from these analyses, summarized in Table 1, indicate that the maximum stress and deformation values are within acceptable limits, ensuring the component’s reliability for electric vehicle applications. This comprehensive approach helps in identifying potential weak points and guides the optimization process for China EV designs.
| Load Case | Maximum Displacement (mm) | Maximum Stress (MPa) |
|---|---|---|
| Maximum Vertical Force | 1.58 | 228.45 |
| Maximum Traction Force | 1.76 | 158.43 |
| Maximum Braking Force | 1.17 | 105.18 |
| Maximum Lateral Force | 0.68 | 140.25 |
In addition to static analysis, modal analysis is performed to determine the natural frequencies of the drive axle housing and avoid resonance with road-induced vibrations. The first six modes correspond to rigid body motions with near-zero frequencies, while higher modes are critical for dynamic performance. The calculated natural frequencies from the 7th to the 12th mode are as follows: 207.00 Hz, 218.09 Hz, 394.81 Hz, 418.68 Hz, 919.21 Hz, and 983.14 Hz. Since typical road excitation frequencies for electric vehicles range from 0 to 50 Hz, these results confirm that the housing will not experience resonance during normal operation, enhancing the safety and comfort of the China EV. This aspect is especially important as electric vehicles often operate in diverse environments, and component durability must be assured.
The core of this study lies in the lightweight design optimization, where we aim to reduce the mass of the drive axle housing without compromising performance. We select the housing thickness (t) and inner diameter (D) as design variables, and employ response surface methodology to model the relationships between these variables and key outputs: mass (M), maximum deformation (δ_max), and maximum equivalent stress (σ_max). A set of 100 experiments is conducted using optimal space-filling design, and the response surfaces are fitted using least squares regression. The general form of the response surface model can be expressed as:
$$
\begin{aligned}
M &= f_1(t, D) \\
\delta_{\text{max}} &= f_2(t, D) \\
\sigma_{\text{max}} &= f_3(t, D)
\end{aligned}
$$
We then apply a multi-objective genetic algorithm (MOGA) to minimize the mass while constraining deformation and stress within allowable limits. The optimization problem is formulated as:
$$
\begin{aligned}
\text{minimize} \quad & M(t, D) \\
\text{subject to} \quad & \delta_{\text{max}}(t, D) \leq \delta_{\text{allow}} \\
& \sigma_{\text{max}}(t, D) \leq \sigma_{\text{yield}}
\end{aligned}
$$
where δ_allow is the permissible deformation based on industry standards, and σ_yield is the yield strength of the material. After convergence, the optimal parameters are determined: a thickness of 20 mm and an inner diameter of 334 mm. This results in a 34.01% reduction in the main body mass (from 42.23 kg to 27.86 kg) and a 14.86% decrease in overall mass (from 96.69 kg to 82.32 kg), demonstrating the effectiveness of the approach for electric vehicle components. Such weight reductions are crucial for improving the energy efficiency and range of China EV models, aligning with global trends in automotive innovation.
To validate the optimized design, we conduct a series of simulation experiments using ANSYS Workbench, comparing the performance before and after optimization. The material properties remain consistent, with a density of 7,850 kg/m³ for 45 steel. The mesh is refined to ensure accuracy, and static analyses are repeated under the same load cases. The results, detailed in Table 2, show that while displacements increase slightly—by up to 7.39% in some cases—they remain within acceptable limits according to relevant standards. Moreover, stresses are reduced in most scenarios, indicating a more uniform stress distribution. This validation underscores the robustness of the lightweight design for electric vehicle applications, particularly in the context of China EV development, where balancing performance and efficiency is key.
| Load Case | Metric | Before Optimization | After Optimization |
|---|---|---|---|
| Maximum Vertical Force | Max Displacement (mm) | 1.58 | 1.61 |
| Max Stress (MPa) | 228.45 | 228.34 | |
| Maximum Traction Force | Max Displacement (mm) | 1.76 | 1.89 |
| Max Stress (MPa) | 158.43 | 150.52 | |
| Maximum Braking Force | Max Displacement (mm) | 1.17 | 1.26 |
| Max Stress (MPa) | 105.18 | 101.02 | |
| Maximum Lateral Force | Max Displacement (mm) | 0.68 | 0.71 |
| Max Stress (MPa) | 140.25 | 143.50 |
Modal analysis is also repeated for the optimized design to assess dynamic characteristics. The natural frequencies are found to be lower than the original values, as shown in Table 3, with the highest reduction of 12.88% in the 7th mode. However, all frequencies remain well above the typical road excitation range of 0–50 Hz, ensuring that resonance is avoided. This is essential for the long-term reliability of electric vehicles, as vibrations can lead to fatigue and failure over time. The successful optimization highlights the potential for further advancements in China EV technology, where lightweighting can contribute to reduced energy consumption and enhanced sustainability.
| Mode | Before Optimization | After Optimization |
|---|---|---|
| 7 | 207.00 | 180.34 |
| 8 | 218.09 | 197.94 |
| 9 | 394.81 | 374.73 |
| 10 | 418.68 | 413.43 |
| 11 | 919.21 | 801.12 |
| 12 | 983.14 | 859.15 |
In conclusion, this study demonstrates a comprehensive approach to optimizing the mechanical components of electric vehicles, specifically the drive axle housing. By integrating 3D modeling, finite element analysis, and multi-objective optimization, we achieve significant weight reductions—34.01% for the main body and 14.86% overall—while maintaining structural integrity and dynamic performance. The optimized design exhibits improved stress distribution and avoids resonance, contributing to the enhanced efficiency and reliability of electric vehicles. As the demand for China EV continues to rise, such innovations in lightweight design will play a pivotal role in advancing the industry, reducing energy consumption, and supporting environmental goals. Future work could explore the application of these methods to other components, further pushing the boundaries of electric vehicle technology.