In modern automotive design, the durability performance of vehicles is a critical factor influencing customer purchasing decisions and overall safety. Statistics indicate that over 80% of automotive component failures are attributed to fatigue damage. Traditional methods for assessing durability, such as proving ground tests, road tests, and bench tests, are time-consuming and costly, often requiring months to complete. In contrast, finite element simulation technology offers significant advantages in reducing development cycles, cutting costs, and enhancing competitiveness. This study focuses on the fatigue analysis of an electric SUV car body using nCode software, with inputs from VPG load spectra to simulate various road conditions. The research aims to identify potential fatigue issues in the early design phase and propose optimization measures to mitigate risks.
The body of a passenger vehicle serves as a key load-bearing structure, susceptible to alternating stresses induced by road irregularities during operation. These stresses can lead to cumulative fatigue damage, potentially causing component failure over time. In this research, I employed a comprehensive approach to analyze the fatigue behavior of an electric SUV body under multiple road conditions, including Belgian roads, ditch sections, forest paths, and twist routes. The study encompasses spot welds, seams, and sheet metal fatigue analysis, providing valuable insights for early-stage design improvements.
Fatigue cumulative damage theory forms the foundation of this analysis. The Miner’s rule, a linear cumulative damage theory, is widely used for evaluating mechanical structures’ fatigue durability. It assumes that failure occurs when the energy absorbed by the structure reaches the fatigue limit. The damage expression is given by:
$$D = \sum_{i=1}^{m} \frac{n_i}{N_i}$$
where $n_i$ represents the actual number of cycles at stress level $i$, and $N_i$ denotes the maximum number of cycles to failure at that stress level. Failure is predicted when $D = 1$. This principle guides the assessment of fatigue life in the electric SUV body components.
To establish the simulation model, I utilized pre-processing software ANSA to develop a finite element model from the CAD design input. The model represents the electric SUV in a fully loaded state, incorporating the body-in-white (TB), occupant masses, and luggage weight. The finite element model was solved using MSC.Nastran (SOL 101) to obtain stress results for strength analysis. These results were then imported into nCode for fatigue analysis, where material properties and fatigue parameters were defined based on VPG road spectrum loads.
The simulation process involves several key steps. First, the finite element model is created, ensuring it reflects the full vehicle load conditions. Mass distribution for occupants and luggage follows specific enterprise standards, simulated using RBE3 elements and mass units. For instance, seat loads are applied at the R-point, while foot and luggage loads are distributed via RBE3 coupling points. Care is taken to avoid weld locations and maintain symmetry in connection ranges. The table below summarizes the mass distribution for the electric SUV in a 2-row, 5-seat configuration:
| Loading Condition | Front Row | Rear Row | Luggage |
|---|---|---|---|
| Heavy Load | 66 kg + 66 kg | 66 kg + 66 kg + 66 kg | 40 kg |
| Medium Load | 66 kg + 66 kg | 42 kg + 42 kg | 40 kg |
| Light Load | 66 kg (driver only) | – | – |
Spot welds are modeled using ACM elements with uniform size and base mesh dimensions. Critical areas, such as shock absorber mounts and ring sections, are carefully checked to ensure RBE3 elements do not share nodes with other connectors. Weld seams are simulated with shell elements, requiring quadrilateral meshes of 4–5 mm in length. Fillet welds and overlap welds are defined with specific thickness parameters: for fillet welds, the weld element thickness $t_w = t_{\text{min}} / 2$, and for overlap welds, $t_w = 2 t_{\text{min}}$, where $t_{\text{min}}$ is the thickness of the thinner plate. The governing equations for weld modeling are:
$$t_w = \frac{t_{\text{min}}}{2} \quad \text{(for fillet welds)}$$
$$t_w = 2 t_{\text{min}} \quad \text{(for overlap welds)}$$
Load cases are defined using the inertia relief method, where unit force loads in the X, Y, and Z directions are applied at key hardpoints, such as the front and rear subframes and shock absorber mounts. The principle of inertia relief involves calculating the structure’s motion under unbalanced external forces to construct a self-balancing system. The finite element equation for static-dynamic balance is:
$$\{F\} + [M]\{\delta\} = 0$$
where $\{F\}$ is the nodal external load vector, $\{\delta\}$ is the nodal acceleration vector, and $[M]$ is the mass matrix given by:
$$[M] = \int_{\Omega} \rho [N]^T [N] d\Omega$$
Here, $[N]$ is the shape matrix, $\rho$ is the density, and $\Omega$ represents the volume integral.
Fatigue analysis is performed in nCode using the stress results from strength analysis, E-N curves for sheet metal, S-N curves for spot weld and seam materials, and input load spectra from VPG. The analysis workflow is structured into five blocks: input loading, material data, fatigue parameters, damage calculation, and result output. The evaluation criteria for the electric SUV are stringent: sheet metal damage must be ≤ 1.0 (except near washers), spot weld damage ≤ 1.0 (or ≤ 2.0 for areas with structural adhesive), and seam damage ≤ 1.0. Critical regions, such as shock absorber surroundings, require extra attention to minimize damage.
The results from the fatigue analysis revealed areas of concern in the electric SUV body. Specifically, the sheet metal at the rear panel lap joint and spot welds connecting the side rear end to the rear floor exhibited excessive damage values greater than 1.0, indicating a high risk of cracking. The damage distribution for sheet metal and spot welds is summarized below:
| Component | Initial Damage | Target | Status |
|---|---|---|---|
| Rear Panel Sheet Metal | 3.64 | ≤ 1.0 | Exceeds |
| Side Rear Spot Welds | 0.7 | ≤ 1.0 | Exceeds |
To address these issues, I implemented structural optimizations. For the sheet metal, strain energy analysis identified a structural discontinuity at the rear panel lap joint, leading to high stress concentration. The design was modified by smoothing the feature to reduce stress peaks under torsional loads. The optimization reduced the sheet metal damage from 3.64 to 0.076, a 48-fold improvement. For the spot welds, the wheelhouse outer panel was extended to increase the lap area, and the two-layer weld was converted to a three-layer weld with an additional spot weld to distribute stress more effectively. This change lowered the spot weld damage from 0.7 to 0.88, a 1.6-fold reduction, meeting the target criteria.
The relationship between impact load and response magnitude can be described using a quadratic equation derived from experimental data. For instance, the maximum response magnitude $S$ (in g) as a function of pendulum height $X$ (in cm) is given by:
$$S = 1.52X^2 – 23.77X + 1125.87$$
This equation helps predict and analyze the response characteristics of the anvil under varying冲击 loads, enabling better design adjustments for the electric SUV body.
In summary, this research demonstrates the effectiveness of using nCode fatigue analysis software in the early development phase of an electric SUV. The VPG load spectra, derived from multi-body dynamics, showed good consistency with actual road test data. The proposed optimization measures significantly reduced fatigue damage in critical areas, ensuring the electric SUV body meets durability requirements. Structural optimization, as applied here, serves as a valuable tool for mitigating fatigue risks in initial design stages. Future work could explore material optimizations and additional load cases to further enhance the electric SUV’s performance.
The integration of finite element simulation and fatigue analysis not only shortens development cycles but also provides a cost-effective approach to improving vehicle reliability. As the automotive industry shifts towards electric vehicles, such methodologies become increasingly important for ensuring the safety and longevity of new models like the electric SUV. This study underscores the importance of proactive fatigue assessment and offers a reference for similar projects in the automotive sector.