Background and Purpose: The stator vibration characteristics are comprehensively mastered by considering the influence of winding and the housing structure on the stator modes. This effect is neglected in the research field of electromagnetic vibration of permanent magnet synchronous motors (PMSMs). Methods: The radial air-gap flux density equations and PMSM’s electromagnetic force density are derived, and then the harmonic characteristics of electromagnetic force density are studied. An equivalent finite element model of the stator is proposed that investigates the impacts of the winding and stator housing rules on the stator modal frequency. Finally, the harmonic response and acoustic analyses of electromagnetic vibration are carried out based on multi-physics field coupling. Results: The equivalent radiated power distribution laws and acoustic field of motor electromagnetic vibration under transient operating conditions are obtained. The theoretical analysis results are consistent with the experimental results. Conclusion: The obtained results show that the spatial order of the radial electromagnetic force is not equal to the order of the radial mode of the motor stator. The reason for this is that structural resonance is induced when the frequency components of the spatial radial electromagnetic force are coupled with the intrinsic frequency of the stator.

The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.

Based on the fact that damping alloys can convert mechanical energy into heat energy and dissipate it in materials and the environment, high-strength Fe-Mn damping alloys were used to replace common gear materials to absorb vibration-generated energy from the vibration source. A high-strength Fe-Mn damping alloy was developed. Taking two kinds of new damping alloy materials and common materials as the research object, a constitutive model of the Fe-Mn damping alloy material was established, the temporal increment step form of the three-dimensional constitutive model was deduced, and a user-defined material (UMAT) subprogram of the damping constitutive alloy model was developed by FORTRAN language. Based on the three-dimensional contact non-linear finite element model of the gear shaft-bearing box, a transient dynamic analysis was conducted using a non-explicit algorithm to obtain the responses of vibration acceleration on the gear reducer box, which can verify the correctness of the proposed constitutive model. The vibration experiments of the gear reducer made of the above-mentioned materials were carried out under different working conditions. The vibration reduction of the developed high-strength Fe-Mn damping alloy can be certificated by comparing the vibration acceleration response and the measuring point temperature of the gear reducer house prepared by different alloys.