Table of Content

25 September 2025, Volume 47 Issue 9 Previous Issue   

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Study on Spatial Preview Tracking Method for Autonomous Vehicle Speed Tracking Xin Guan,Sishen Li,Xin Jia 2025, 47 (9):  1647-1654.  doi: 10.19562/j.chinasae.qcgc.2025.09.001 Abstract ( 394 )   HTML ( 36 )   PDF (2911KB) ( 197 )   Save Speed tracking is one of the most important function of autonomous vehicles. In order to improve the spatial speed tracking accuracy of autonomous vehicles at the designated spatial position， in this paper a spatial preview method for autonomous vehicles speed tracking is proposed. Compared with other methods， this paper focuses on spatial preview rather than temporal preview. Firstly， a motion primitives based spatial multi-segment speed preview method is established. The target speed is spatially previewed by motion primitive， and the segmentation points are determined according to the preview tracking error. The distance of each segment is adjusted dynamically to ensure that the preview tracking error within the allowable deviation range. The expected longitudinal acceleration is determined according to the preview result. Then， a longitudinal acceleration tracking method is established to track the expected longitudinal acceleration. Finally， the effectiveness of the method proposed in this paper is verified and compared with various currently widely used methods in the simulation environment. The test results show that the method proposed in this paper has higher spatial tracking accuracy compared with other methods. Figures and Tables | References | Related Articles | Metrics

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FPGA Hardware-Accelerated Implementation of Model Predictive Path Tracking Control for Autonomous Vehicles Wenchang Li,Zhiguo Zhao,Kaichong Liang,Kun Zhao,Qin Yu 2025, 47 (9):  1655-1664.  doi: 10.19562/j.chinasae.qcgc.2025.09.002 Abstract ( 286 )   HTML ( 17 )   PDF (4301KB) ( 80 )   Save For the high complexity of online solving in model predictive control （MPC） and its challenges in real-time implementation on existing autonomous vehicle onboard controllers， an FPGA hardware-accelerated implementation of MPC-based path tracking method is proposed in this paper. Firstly， an MPC-based path tracking controller for autonomous vehicles is designed. Then， to simplify the solution process， the MPC problem is transformed into a constrained quadratic programming problem， and the Hildreth method is introduced for solving it. Furthermore， to improve the real-time performance and deployment efficiency of the control algorithm， a convenient FPGA implementation scheme for the MPC path tracking algorithm is developed based on the Xilinx System Generator tool. Finally， MATLAB/Simulink-CarSim co-simulation and hardware-in-the-loop （HIL） tests are conducted under different conditions. The results show that the proposed method enables autonomous vehicles to accurately track the desired path， with an average FPGA computation time of less than 0.1 ms， validating its effectiveness and real-time performance. Figures and Tables | References | Related Articles | Metrics

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Study on Unsupervised Learning Method for Roadside 3D Object Detection Based on Cross Domain Spatiotemporal Feature Matching Wei Gong,Yafei Wang,Bowen Wang,Zexing Li,Jiaming Sun 2025, 47 (9):  1665-1673.  doi: 10.19562/j.chinasae.qcgc.2025.09.003 Abstract ( 192 )   HTML ( 8 )   PDF (4742KB) ( 58 )   Save Roadside 3D target detection provides wide-view traffic information， effectively enhancing single-vehicle autonomous driving. In general， deploying a perception system at a new intersection requires a large amount of data collection and manual labeling to ensure the detective accuracy of the training model， which is time-consuming and costly. For the above-mentioned problems， in this paper an unsupervised domain adaptation （UDA） algorithm is proposed for roadside application， which achieves efficient knowledge transfer and accurate 3D object detection by matching cross-domain spatiotemporal features between high-quality labeled roadside data and unknown roadside scenes. Firstly， a source-domain training model （RoadPillars） is constructed that balances cross-domain data distributions， effectively reducing overfitting of the model to the original data distribution and improving model generalization. Moreover， a cross-domain transfer scheme is designed that ensures spatial consistency of continuous sequences for stable and robust UDA. The experimental results on three distinct roadside scenes and LiDAR types from public datasets demonstrate that the unsupervised domain adaption algorithm proposed in this paper achieves an average accuracy improvement of 24.2% and 8.0% over state-of-the-art approach， significantly improving the generalization and reliability of roadside perception. Figures and Tables | References | Related Articles | Metrics

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End-to-End Decision-Making Model Based on Reinforcement Learning Incorporating Bird's Eye View Representation Baixue Tang,Yingfeng Cai,Long Chen,Hai Wang,Zhongyu Rao,Ze Liu 2025, 47 (9):  1674-1685.  doi: 10.19562/j.chinasae.qcgc.2025.09.004 Abstract ( 197 )   HTML ( 1 )   PDF (4012KB) ( 66 )   Save End-to-end autonomous driving decision-making and planning models are a hot research direction in the industry. The spatial and temporal inconsistency between sensor signals and action outputs， as well as the convergence issues of end-to-end models， greatly limit the practical application effectiveness of these models. Therefore， in this paper an end-to-end reinforcement learning model called FB-Roach is proposed that integrates bird's-eye view prediction. Environmental information representation is established through a bird's-eye view prediction model. A forward projection module centered on a static Look-Up table， as well as a multi-task backward projection module that integrates temporal information， depth embedding， and semantic embedding， is designed to ensure the consistency between input signals and output actions. Furthermore， by innovatively incorporating the attention mechanism， the non-recurrent deep network architecture is proposed that effectively fuses bird's-eye view and vehicle state information. The model's action output is optimized using the PPO reinforcement learning algorithm to achieve intelligent decision-making and control for autonomous vehicles. Based on the CARLA simulator， a variety of quantitative evaluation indicators are constructed under different benchmarks. The experiments results show that the proposed algorithm outperforms current mainstream algorithms in terms of model convergence speed and driving decision safety. Figures and Tables | References | Related Articles | Metrics

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Deep Reinforcement Learning Control of Multi-dimensional Coupled Stability of Off-road Vehicles Guang Xia,Shibiao Wu,Yang Zhang,Heng Wei,Xianyang Liu 2025, 47 (9):  1686-1699.  doi: 10.19562/j.chinasae.qcgc.2025.09.005 Abstract ( 255 )   HTML ( 13 )   PDF (5727KB) ( 108 )   Save The operation of off-road vehicles in a variety of extreme conditions can result in instability risks， including lateral slip， longitudinal slip， and roll. Nevertheless， there are difficulties in defining the stability states of the vehicle with a precise mathematical model. Therefore， in this paper a multi-dimensional coupled stability deep reinforcement learning collaborative control strategy for off-road vehicles under extreme conditions is proposed. Firstly， indicators for evaluating stability in different dimensions are established. Concurrently， the multi-dimensional coupled stable domain is constructed， and the stable domain is divided based on the coupling relationship between tire lateral and vertical forces， and the boundary parameters between each stable domain are determined through offline tire model training. Secondly， the DDPG （Deep Deterministic Policy Gradient） deep reinforcement learning algorithm is used to construct control strategies for the interaction between off-road vehicles and the environment. The optimal weight coefficients of each dimension are output to characterize the stability status of off-road vehicles. Then， a controller for longitudinal， lateral， and roll control is designed based on a collaborative control strategy for decoupling the car chassis. Finally， the joint simulation and hardware-in-the-loop verification in CarSim and Simulink show that the multi-dimensional coupled stability control strategy based on the DDPG algorithm markedly enhances the overall stability of the vehicle. Figures and Tables | References | Related Articles | Metrics

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Research on MOT SLAM Algorithm Based on Scene Flow Uncertainty Model Jiajing Su,Yuan Zhu,Ke Lu 2025, 47 (9):  1700-1711.  doi: 10.19562/j.chinasae.qcgc.2025.09.006 Abstract ( 115 )   HTML ( 2 )   PDF (3421KB) ( 17 )   Save Multi-object Tracking （MOT） combined with Simultaneous Localization and Mapping （SLAM） ， making full use of dynamic and static information in the scene， can improve positioning accuracy and robustness， which has received considerable attention. In this paper a 3D object tracking SLAM algorithm based on the scene flow uncertainty model is proposed. With stereo or RGB-D images as input， combining instance masks and IMU information， it can accurately detect dynamic features and jointly estimate the pose transformation of itself and the objects. For the problem that dynamic， static and temporary static features cannot be accurately identified， instance information and scene flow uncertainty modeling are combined for modeling to eliminate error interference and achieve accurate dynamic feature detection. For the problem that feature points of moving objects are scarce and difficult to track， KLT optical flow and instance information are combined to perform robust multi-level data association. By constructing a factor graph and introducing vehicle kinematic constraints， tightly coupled optimization of the vehicle's own and moving object poses and map point coordinates is achieved. Finally， comparative experiments are conducted on public datasets. The results show that the proposed algorithm can accurately track the pose transformation of itself and the moving objects. Figures and Tables | References | Related Articles | Metrics

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LiDAR-Visual Fusion SLAM System for High-Dynamic Environment Yunshui Zhou,Chengyu Gao,Shengjie Huang,Runbang Zhang,Xin Chen,Yougang Bian,Hongmao Qin 2025, 47 (9):  1712-1720.  doi: 10.19562/j.chinasae.qcgc.2025.09.007 Abstract ( 200 )   HTML ( 1 )   PDF (2913KB) ( 41 )   Save Accurate localization and mapping are critical for autonomous driving systems. However， single-sensor Simultaneous Localization and Mapping （SLAM） systems often struggle to operate reliably across different environment， particularly in highly dynamic scenes where dynamic obstacles can degrade accuracy or even cause system failure. Therefore， in this paper a LiDAR-Visual fusion SLAM framework tailored for high-precision mapping and localization problems in dynamic environment is proposed. Firstly， an odometry method that fuses sparse LiDAR point clouds with dense image data is designed， leveraging the high-precision ranging capabilities of LiDAR and the rich information provided by images to enhance odometry accuracy. To address challenges in highly dynamic scenes， based on a real-time image semantic segmentation network， BiSeNetV2， combined with motion feature detection techniques based on inter-frame and multi-frame sequences， the efficient and accurate identification of dynamic points among the 3D feature points obtained from the LiDAR-Visual fusion is realized， which are removed from the map to mitigate the influence of dynamic obstacles. Tests are carried out on the nuScenes autonomous driving dataset， and the results show significant improvement of the proposed system in accuracy and robustness of localization and mapping in dynamic environment. Figures and Tables | References | Related Articles | Metrics

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Quantitative Assessment of Vehicle-VRUs Collision Risk at Intersection with Visual Obstacle Yong Han,Jiale Zhang,Di Pan,He Wu,Li Xu 2025, 47 (9):  1721-1730.  doi: 10.19562/j.chinasae.qcgc.2025.09.008 Abstract ( 186 )   HTML ( 1 )   PDF (3429KB) ( 35 )   Save For the collision risk between vehicles and vulnerable road users （VRUs） at intersections with visual occlusions， in this study a driving risk assessment method integrating road environment characteristics is proposed. Based on 831 accident videos from the VRU-TRAVi （Vulnerable Road Users Traffic Accident database with Video）， K-modes clustering is used to extract three typical scenarios： signalized intersections， unsignalized intersections， and warning signal intersections. Through variability analysis， the study reveals significant correlation between kinematic parameters （vehicle speed VSpd， obstacle speed OSpd， vehicle acceleration VAcc， and obstacle acceleration OAcc） and road environment features. A risk assessment model （Urfr） is developed by setting safety thresholds based on the median values of kinematic parameters in clustered scenarios and incorporating road feature weights. The results show that： at traffic signalized intersections， the highest risk of driving occurs when the obstacle speed OSpd = 0， vehicle speed VSpd ≥ 45 km?h-1， acceleration VAcc ≥ 0. At unsignalized intersections， the highest risk of driving occurs when the obstacle OSpd = 0， vehicle VSpd ≥ 35 km?h-1， VAcc ≥ 0. At warning signalized intersections， the driving risk is highest when the obstacle speed OSpd ≤ 10.29 km?h-1， acceleration OAcc ≤ 0， and the vehicle VSpd ≥ 38 km?h-1， VAcc ≥ 3.74 m?s-2. The model quantifies the impact of road environment features on kinematic parameters， providing a theoretical foundation for risk prediction and active control of autonomous vehicles in visually occluded intersection scenarios. Figures and Tables | References | Related Articles | Metrics

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Adaptive Suspension Parameters-Based State Estimation Using Road Classification Jiawei Shi,Hao Chen,Zhifei Zhang,Zhongming Xu,Kanlun Tan,Li Yang 2025, 47 (9):  1731-1741.  doi: 10.19562/j.chinasae.qcgc.2025.09.009 Abstract ( 206 )   HTML ( 7 )   PDF (2927KB) ( 52 )   Save Accurate acquisition of relative suspension velocity information is crucial for improving vehicle ride comfort and stability. Road classification can provide prior information for suspension state estimation and control， thereby enhancing the accuracy of suspension state estimation. Therefore， in this paper an adaptive suspension parameters-based Kalman filter algorithm is proposed based on road classification. The algorithm adjusts the characteristics parameters of the suspension model and the covariance matrix of the Kalman filter according to the road classification results， so as to obtain accurate relative suspension velocity information. Firstly， a road classification method based on a Long Short-Term Memory neural network is designed using signals from the vehicle’s Inertial Measurement Unit. Next， a 7-degree-of-freedom vehicle suspension system model is constructed with road surface roughness as the input. A genetic algorithm is employed to identify the spring stiffness and damper coefficient for different road classes， resulting in an adaptive suspension parameter model and determining the process noise covariance matrix for each class. Finally， based on the road classification results， the corresponding suspension parameters and process noise covariance matrix are applied in the Kalman Filter to estimate the vehicle's relative suspension velocity. The MATLAB-ADAMS/Car co-simulation results show that compared with the Adaptive Kalman Filter （AKF） method， the proposed method improves the root mean square error of the estimated relative suspension velocities by 28.4%， 22.8%， 19.7%， 7.3%， and 24.1% for road classes ISO-A， ISO-B， ISO-C， ISO-D， and real-road conditions， respectively. The correlation coefficients of the estimated relative suspension velocities improve by 11.8%， 5.8%， 2.8%， 1.1%， and 7.2% for the same conditions， verifying the effectiveness and feasibility of the method. Figures and Tables | References | Related Articles | Metrics

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A Vibration Transfer Path Analysis Method Based on Entropy Singular Value Decomposition Lingchen Kong,Xiaolei Yuan,Xuan Zhao,Qiang Yu,Chenyu Zhou,Rong Huang 2025, 47 (9):  1742-1751.  doi: 10.19562/j.chinasae.qcgc.2025.09.010 Abstract ( 149 )   HTML ( 2 )   PDF (4002KB) ( 30 )   Save Operational transfer path analysis （OTPA） is an important way to identify the key transfer paths of complex mechanical systems， but the signal crosstalk between structures seriously affects the analysis accuracy of OTPA. In this paper， the traditional truncated singular value decomposition （TSVD） method is improved based on the entropy theory， and the operational transfer path analysis model based on the entropy singular value decomposition method （OTPA-ESVD） is constructed. In the proposed model， the singular value entropy is used to evaluate the information of eigenvalues， and then the principle of high-contribution pairing is used to design a more comprehensive singular value selection method， which overcomes the dependence on engineering experience of conventional TSVD. In this paper， an actual electric vehicle test is designed to verify the OTPA-ESVD model by comparing it with the operational transfer path analysis model based on the median singular value decomposition （OTPA-MSVD）. The results show that the performance of the OTPA-ESVD model is better than the OTPA-MSVD model under different driving conditions， and the reconstructed target signal of the OTPA-ESVD model is closer to the actual measured signal， with optimization of no less than 13.8% and 8.1% in the comparison of the error root mean square value and the amplitude of the key frequency， respectively. Figures and Tables | References | Related Articles | Metrics

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Study on Effect of Turbulence Model Coefficients on Accuracy of Aerodynamic Simulation Wenqiang Shang,Fengli Zhang,Wenjiang Wang,Qiuli Luo,Peng Zhou 2025, 47 (9):  1752-1762.  doi: 10.19562/j.chinasae.qcgc.2025.09.011 Abstract ( 158 )   HTML ( 7 )   PDF (6872KB) ( 53 )   Save The shear-stress-transport （SST） k-\omega turbulence model is one of the best comprehensive two-equation turbulence models available and is widely used in the research of automotive aerodynamics. The existing studies have shown that the closure coefficients in the \mathrm{S}\mathrm{S}\mathrm{T}\text{?}k-\omega turbulence model significantly affect the accuracy of aerodynamic simulation for vehicles. However， most researches are limited to simplified models. Therefore， in this paper， the impact of the typical turbulence model coefficients on the accuracy of aerodynamic simulations of a sedan is studied under stationary wheel conditions. It is found that the turbulence model coefficients {\sigma }_{\omega \mathrm{1}},\text{?}{\sigma }_{\omega \mathrm{2}},\text{?}{\beta }^{*}and C_{\mathrm{T}} are linearly related to aerodynamic drag and lift， while approximately quadratic for a_{\mathrm{1}}. Furthermore， this paper uncovers the influence mechanism of turbulence model coefficients on the flow field around the vehicle， and the recommended values for turbulence model coefficients to improve the accuracy of aerodynamic simulation are provided： {\sigma }_{\omega \mathrm{1}}=\mathrm{0.3}， {\sigma }_{\omega \mathrm{2}}=\mathrm{0.7}， {\beta }^{*}=\mathrm{0.06}， a_{\mathrm{1}}=\mathrm{0.5} and C_{\mathrm{T}}=\mathrm{1.0}.This paper provides theoretical and engineering guidance for more accurate and reliable aerodynamic simulation of vehicles. Figures and Tables | References | Related Articles | Metrics

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Modeling and Characteristics Analysis of Oil Charging and Discharging System of Hydraulic Auxiliary Braking System Maohan Xue,Yao Fu,Xiaohu Geng,Shaohua Sun,Yulong Lei 2025, 47 (9):  1763-1772.  doi: 10.19562/j.chinasae.qcgc.2025.09.012 Abstract ( 158 )   HTML ( 3 )   PDF (6474KB) ( 21 )   Save Parallel hydraulic retarder is an auxiliary braking device in the heavy-load and long-term downhill process of commercial vehicles. The complex and difficult physical changing process inside the working chamber determines the response time and braking torque. The equivalent hydraulic model is established in this paper to describe the nonlinear coupling relationship between the inputting control pressure and the outputting braking characteristics of the oil filling and discharging system， and the relationship between the input and output is clarified， which is the intermediate variable "liquid filling rate". It is found that with the increase of vehicle speed， the amount of oil in the working chamber decreases and the braking torque eventually decreases. When the pressure is 2.5 bar， the braking torque of hydraulic retarder reaches the maximum of 4 044 N·m at 1 290 r/min， and then the braking torque decreases with the increase of rotating speed. Meanwhile， the model can effectively predict the response time of oil filling and discharging， and the maximum error between simulation and experiment is 16.12%. The changing process of oil charging and discharging system is accurately analyzed and the relevant characteristics are obtained in this paper， which can assist the structural design of hydraulic retarder and the control of vehicle braking process. Figures and Tables | References | Related Articles | Metrics

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Study on Quasicrystal-Based Solid-State Thermal Diodes and Application in Thermal Management Jinjia Zhang,Han Wen,Yong Liu,Ke Wang 2025, 47 (9):  1773-1781.  doi: 10.19562/j.chinasae.qcgc.2025.09.013 Abstract ( 156 )   HTML ( 6 )   PDF (3095KB) ( 21 )   Save Thermal diodes， with unidirectional heat transfer properties， have great potential in the thermal management systems of electric vehicles. In this study， based on a large materials property database， the optimal material combinations are screened to construct high-performance composite solid-state thermal diodes. Through one-dimensional model and finite element simulation， the results show that the thermal rectification ratio of the optimized quasicrystal-based solid-state thermal diode can reach 3.2， significantly improving thermal management efficiency. This research provides key theoretical and practical insights for applying solid-state thermal diodes in electric vehicle thermal management. Figures and Tables | References | Related Articles | Metrics

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Single-phase Liquid Cooling Characteristics of High-Power Fast-Charging Cables for Electric Vehicles Zhilong Zhou,Gangtao Liang 2025, 47 (9):  1782-1789.  doi: 10.19562/j.chinasae.qcgc.2025.09.014 Abstract ( 194 )   HTML ( 0 )   PDF (3717KB) ( 29 )   Save With the rapid increase in the number of electric vehicles， the pain points of slow charging and difficult charging of electric vehicles have become more and more obvious. In order to meet the rapid charging demand of electric vehicles， high-power fast charging technology has emerged. However， the elevated charging current will precipitate a precipitous increase in the temperature of charging cable， and its cooling problem has become a significant challenge in the development of high-power fast charging technology for electric vehicles. In this study， the effect of liquid cooling channel arrangement， coolant mass flow rate， and inlet subcooling degree on the temperature distribution of cables are comparatively analyzed by numerical simulation. The results show that when the core cross-sectional area and liquid-cooling channel cross-sectional area remain constant， the cooling effect of liquid-cooling channel external configuration on charging cables is better than that of internal configuration. Increasing the inlet subcooling degree can decrease the temperature of charging cable， however， there exists an optimal mass flux of 500 kg/（m2·s）. Under the working conditions specified in this study， the liquid-cooled cable is capable of withstanding a maximum current of 1 250?A， which is ten times of a standard cable. Figures and Tables | References | Related Articles | Metrics

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Design and Verification of Vehicle TSN Traffic Scheduling System Jingyao Qu,Sen Zhao,Lijin Zhao,Lihua Yang,Jun Huang,Hongwei Yin,Wei Chang 2025, 47 (9):  1790-1802.  doi: 10.19562/j.chinasae.qcgc.2025.09.015 Abstract ( 201 )   HTML ( 0 )   PDF (7710KB) ( 28 )   Save As the automotive industry continues to advance in intelligence and connectivity， vehicle functions are becoming increasingly complex， driving the regional electronic and electrical architectures becoming the mainstream approach. In such complex regional architectures， ensuring the reliability， real-time performance， and determinism of data interaction is particularly critical. Time-Sensitive Network （TSN） protocol suite has gained significant attention for its ability to achieve deterministic minimal latency in non-deterministic Ethernet environment. To address the inability of traditional Ethernet to meet deterministic low-latency requirements， in this paper the time-aware scheduling mechanism defined by the IEEE 802.1Qbv protocol in TSN （Time-Sensitive Networking） technology is introduced. By employing RTaW network simulation and an S32G hardware system implementation， the solution is adapted to the third-generation automotive electrical/electronic （E/E） architecture. It achieves distributed traffic scheduling through a regional controller + TSN switch design， utilizing gate control lists （GCL） to enable staggered transmission and coordinated scheduling of multiple types of periodic data streams. The simulation results show that， under the time-aware gated scheduling mechanism， the critical flow in the in-vehicle network achieves a 17% reduction in average end-to-end delay， a 99.2% decrease in jitter， and an 86% improvement in maximum waiting delay. Figures and Tables | References | Related Articles | Metrics

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Study on Internal Flow and Heat Dissipation Characteristics of Oil-Cooled Electric Motors Based on Multiphase Flow Model Yichen Zhu,Yuwei Sun,Sihua Liu,Jianghaoyu Yan,Li Zhai,Mindi Zhang 2025, 47 (9):  1803-1813.  doi: 10.19562/j.chinasae.qcgc.2025.09.016 Abstract ( 160 )   HTML ( 5 )   PDF (8124KB) ( 31 )   Save Oil cooled motor has attracted much attention in recent years because of its compact design and good cooling performance. In order to improve the heat dissipation effect of the motor， in this paper the multiphase flow model based on VOF is used to conduct numerical simulation research on two kinds of oil-cooled permanent magnet synchronous motors. Firstly， by comparing the simulation results of the two motors with the experimental results under the rated working conditions， it can be seen that the maximum temperature relative error is less than 5%， which proves that the numerical method is accurate and reliable. Then， the research on the internal oil flow and cooling heat dissipation characteristics of the motor cooling system is carried out under rated working conditions. The results show that the highest temperature of the winding and stator of motor I is 98 and 93.8 ℃ respectively， with the temperature non-uniformity of 4.28% and 5.48% respectively. The highest temperature of the winding and stator of the motor II is 93.0， 92.5 ℃， with the temperature non-uniformity of 3.62% and 5.08%， respectively. At the same time， it is found that although the rotor oil dumping cooling method can make the oil distribution more uniform and improve the cooling efficiency of the cooling oil， it will increase the starting time of the motor cooling system. The study provides theoretical basis for subsequent optimization design of efficient and reliable oil-cooled cooling system. Figures and Tables | References | Related Articles | Metrics

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Research on Automotive Calibration Technique for Aluminum Honeycomb Cell Scaling CAE Model Based on Machine Vision Enze He,Guojie Wang,Zhi Fu,Aimin Shi 2025, 47 (9):  1814-1825.  doi: 10.19562/j.chinasae.qcgc.2025.09.017 Abstract ( 133 )   HTML ( 4 )   PDF (5590KB) ( 24 )   Save To promote the aluminum honeycomb cell scaling CAE model calibration efficiency， which is mainly affected by the difficulty of deformation mode recognition， the plastic strain contour plot vision based deformation mode criterion is proposed in this paper. The new method， along with the traditional stiffness criterion， is integrated into an automated script by python， which is applied in the ACMDB aluminum honeycomb cell scaling CAE model calibration process. The results show that the proposed deformation mode criterion is highly correlated with the aluminum honeycomb compression deformation mode， and the developed automated script can effectively reduce the dependence of the calibration process on engineers. Figures and Tables | References | Related Articles | Metrics

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Research on the Dynamic Mechanical Properties of 3D Printed Voronoi Gradient Foam Material Geng Luo,Zhaofei Zhu,Yaozhi Xiao,Yisong Chen 2025, 47 (9):  1826-1839.  doi: 10.19562/j.chinasae.qcgc.2025.09.018 Abstract ( 176 )   HTML ( 4 )   PDF (11080KB) ( 39 )   Save The integration of gradient design into foam materials offers a promising approach to regulating their mechanical properties and improving energy absorption. In this study， foam materials with both layered and continuous gradient structures are designed and fabricated using Voronoi diagrams and 3D printing technology. A finite element model， which considers the strain rate of the matrix material， is developed and validated through quasi-static compression tests. The dynamic mechanical properties and deformation behaviors of the gradient foam materials are subsequently investigated. The results reveal that for foams with a positive gradient， under varying loading velocities， the lower-density end （i.e.， the loading end） deforms first and the deformation propagates toward the support end， forming a distinct plastic shock wave， referred to as the forward single-wave mode. In contrast， for foams with a negative gradient， as the loading velocity increases， they sequentially exhibit reverse single-wave mode， double-wave mode， and forward single-wave mode. The deformation modes of layered gradient and continuous gradient foams are similar， with the difference diminishing as the number of layers increases. The theoretical models for single-wave and double-wave behaviors of gradient foams， based on stress wave theory， accurately predict the stresses at both the impact and support ends under different impact velocities. Furthermore， the analysis of the critical velocity for negative gradient foams indicates that when the gradient rate is small， the support end， with its larger cells， is more likely to deform， thus reducing the inertial effect associated with increased loading velocities. This leads to an increase in the second critical velocity range for the double-wave mode， that is， the second critical velocity increases， while the first critical velocity exhibits lower sensitivity to the gradient rate. With the rise in impact velocity， the energy absorption performance of negative gradient foams is significantly enhanced. Figures and Tables | References | Related Articles | Metrics

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Research on High Strength Mg-4Y-3Zn-2Al Alloy Formed by Ultrasonic Vibration Continuous Squeeze Casting-Extrusion Shilong Li,Jianyu Li,Peijie Xiao,Shiwei Xu,Kewang Xiang,Shusen Wu,Shulin Lü 2025, 47 (9):  1840-1846.  doi: 10.19562/j.chinasae.qcgc.2025.09.019 Abstract ( 171 )   HTML ( 2 )   PDF (155526KB) ( 75 )   Save The effect of ultrasonic treatment on the microstructure and properties of Mg-4Y-3Zn-2Al （wt.%） magnesium alloy formed by continuous squeeze casting-extrusion is investigated. The changes in microstructure and mechanical properties of the as-cast and extruded alloys before and after ultrasonic treatment are investigated by XRD， DSC， SEM， EBSD， and quasi-static tensile tests. The results show that ultrasonic treatment significantly refines the size of the primary Al2Y phase and eliminates the agglomeration of the primary phase. After continuous squeeze casting-extrusion， the grain size is refined to 2 μm， resulting in a fully recrystallized equiaxed grain structure. After ultrasonic treatment， the yield strength and tensile strength of the extruded alloy is 246.8 and 307.5 MPa， respectively， with an elongation of 17%. Compared with the non-ultrasonic treatment alloy， its elongation is greatly improved （by 95%） while the strength also increases， achieving a good strength-plasticity match. Figures and Tables | References | Related Articles | Metrics