## 2 The Optimization Design of Parameters and Parallel Wheel Travel Simulation
Based on the analysis of the lateral slip results in the figure, it is observed that the lateral slip of the wheel varies within the range of \(-1.8\) mm-\(3.8\) mm, with a total variation of \(5.6\) mm. It is generally recommended to control the lateral slip of the wheel within a range of \(5\) mm for optimal performance during wheel travel. Consequently, optimization measures are required to meet this criterion. 2. The ideal range for camber angle variation is typically \(0^{\circ}\)-\(1^{\circ}\). In Figure 2(b), the variation range of the camber angle during the wheel bounce travel is \(-0.17^{\circ}\) to \(0.1^{\circ}\), and its variation falls within the ideal range, meeting the design requirements. 3. In Figure 2(c), within the wheel bounce travel range of \(-50\) mm-\(50\) mm, the caster angle varies within the range of \(0.35^{\circ}\)-\(0.52^{\circ}\), which is generally under the design requirements. 4. The reasonable variation range of kingpin inclination angle is typically between \(5^{\circ}\) and \(15^{\circ}\), with \(5^{\circ}\) being considered optimal for many off-road vehicles. From Figure 2(d), it is observed that the caster camber varies within the range of \(6.25^{\circ}\)-\(7.3^{\circ}\). Although the variation falls within the requirements, there is still room for optimization. 5. In Figure 2(e), it is observed that the variation range of the front toe angle is between \(0.38^{\circ}\) and \(-0.42^{\circ}\), indicating a relatively small and acceptable range, and the variation trend aligns with the design requirements. Figure 2: Model of the double-wishbone front suspension. Through the analysis results, the absolute value of the lateral slip at the wheel contact point reached 5.6 mm. Such a large lateral slip increased tire wear. To control the lateral slip of the double-wishbone front suspension of the mining dump truck, if the slip amount is within a reasonable range, parameterization and optimization analysis of key suspension points are required. Due to the mutual influence between various suspension performance parameters, to avoid the situation where one performance is improved and other properties have deteriorated, this paper selects the changes in camber angle \(\psi\), toe angle \(\theta\), caster angle \(\bar{\beta}\), the change in the kingpin inclination angle \(\alpha\) and the standard deviation of the change in the side slip amount \(S_{\text{lat}}\) of the front suspension tire contact point. These five performance parameters are used as optimization targets, and their respective objective functions are as follows:  
\[ \begin{array}{l}\text{min}f_{1}(X)=\text{min}(\text{max}|\psi|)\\ \text{min}f_{2}(X)=\text{min}(\text{max}|\theta|)\\ \text{min}f_{3}(X)=\text{min}(\text{max}|\alpha|)\\ \text{min}f_{4}(X)=\text{min}(\overline{\beta})\\ \text{min}f_{5}(X)=\text{min}\sigma(S_{\text{lat}})\end{array}  \tag{19}\]  
According to the unified objective method, the above sub-objectives are combined into a unified objective function as shown in Formula (20):  
\[\text{min}f(X)=\text{min}\sum_{j=1}^{5}w_{j}f_{j}(X) \tag{20}\]  
Figure 3: Changes with the wheel travel. According to the characteristics of the suspension and the technical indicators of the car body, the z-coordinates of the front, rear, and outer points of the upper and lower wishbones of the front suspension are parameterized, and design variables are created accordingly. Due to the technical specifications and structural space of the vehicle itself, this variable of the suspension structure's hard point parameters cannot be too large. The constraints are in the form of percentage relative values, which are \(-10.0\) and \(+10.0\), respectively. Considering the numerous hard-point coordinates present in a suspension design, comprehensively addressing all of them will significantly impact optimization speed. Therefore, ADAMS/Insight was used for sensitivity analysis of hard point coordinates, and key influencing factors were selected according to the size of the influence to optimize them.