The steering knuckle is a crucial component of student racing vehicles, designed by the Formula Society of Automotive Engineers (FSAE). Developing a lightweight (LW) vehicle that meets the requirements of the student formula car presents a challenge. This study presents a LW design of the steering knuckle using the Topology Optimization (TO) approach for the Formula Society of Automotive Engineers (FSAE) competition considering two different mass constraints (40% and 48%). Moreover, the research includes Stress–Life (SN) curves for three materials, structural steel, 4130 steel, and AISI 1020 steel, providing essential insights into the fatigue characteristics of the model. The results compare the three materials and two mass reduction levels, with steel 4130 achieving a significant mass reduction of 42.70%. Additionally, steel 4130 exhibits superior performance in weight reduction, stress, deformation, and safety aspects. The optimized design meets the criteria for strength, stiffness, and safety under various conditions. The fatigue analysis reveals that AISI 1020 and steel 4130 have superior endurance (1 × 10⁵ and 2 × 10⁵ cycles, respectively). This research provides significant contributions to the development of a LW, high-performance steering knuckle for student formula racing vehicles, highlighting the significance of TO and material selection in achieving optimal outcomes.

With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers’ purchases are different for different vehicle brands. However, the customer’s evaluation of the vehicle experience has a greater influence on the customer’s purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers.

The magnitude of core loss is a crucial factor affecting the efficiency of power converters. Due to the complex mechanism of core loss, diverse influencing factors, and the strong coupling characteristics between materials and operating conditions, traditional core loss prediction models struggle to achieve high-precision prediction of core loss. Based on the Artificial Neural Network (ANN), this paper investigates core loss under high-frequency sinusoidal excitation. The core loss training data is processed using a logarithmic transformation method, and an ANN core loss prediction model is established with temperature, frequency, and magnetic flux density as features. The results show that, compared with non-logarithmic processing, logarithmic transformation of the data can effectively improve the prediction accuracy (PA) of the ANN model. Within the ±10% error range, the maximum PA of the ANN prediction model reaches 98.48%, and the minimum Mean Absolute Percentage Error (MAPE) can be as low as 2.58%. In addition, a comparison with the Steinmetz Equation (SE) and K-nearest neighbor (KNN) prediction models reveals that, for four materials, within the ±10% error range of the true core loss values, the minimum PA of the ANN model is 93.33% with an average of 95.38%; the minimum PA of the KNN model is 43.94% with an average of 62.07%; and the minimum PA of the SE model is 14.91% with an average of 19.83%. Furthermore, the MAPE of the ANN model is within 5%.

Autonomous valet parking technology refers to a vehicle’s use of onboard sensors and line-controlled chassis to carry out a fully automatic valet parking function, which can greatly improve the driver’s experience. This study focuses on autonomous parking, employing environmental modeling and vehicle kinematics models. Innovatively applying the PSBi-RRT algorithm to path planning in autonomous parking systems constitutes this research’s contribution to this field. Firstly, the environment is modelled by the raster method; then, the PSBi-RRT algorithm is used for path planning, and a B-spline curve is used for path optimization. Speed and acceleration are smoothed at the same time, and finally, a smooth and obstacle-avoiding path planning scheme is obtained. The results show that an autonomous parking system based on the PSBi-RRT algorithm performs path planning from the vehicle to the parking space. Compared to RRT and Bi-RRT, PSBi-RRT generates shorter planning paths, smoother heading angle changes, shorter planning times, fewer nodes, and higher success rates. This research provides theoretical support for the development of autonomous parking technology.

The squeeze‐film damping (SQFD) is an important dissipation mechanism of Micro‐Electro‐Mechanical Systems resonators. The current SQFD models of perforated plates treat borders of plate and holes as the constant pressure boundary, without considering border effect. In this paper, the border effect on SQFD is studied by expanding simulation area. At the same time, based on the research of non‐perforated plate border effect, the calculation size of the perforated plate hole cell is modified, and the modified SQFD model of the perforated plate has been built. Compared with simulation results, it shows the border effect has a great influence on SQFD of perforated plate. The precision of the modified model is higher than that of recent models. For a rectangular plate, the maximum error of the modified model is 12%, while for the recent model it is 40%. For a circular plate, the modified model is 38%, while the recent model is 58%.