Table of Content

25 August 2025, Volume 47 Issue 8 Previous Issue   

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Research on Construction of Autonomous Driving Simulation Scenario Based on the Traffic Rule Model Mingyue Ma,Zelin Miao,Weiqing Wang,Guangming Zhao,Changjun Wang 2025, 47 (8):  1437-1447.  doi: 10.19562/j.chinasae.qcgc.2025.08.001 Abstract ( 400 )   HTML ( 45 )   PDF (1455KB) ( 162 )   Save In this paper， a universal and extensible description model for natural language road traffic rules is proposed， which transforms the language description of traffic rules into logical propositions through ontology methods. Then a unified road traffic rule element layer based on design operation domain and dynamic driving task is established. A road traffic rule test boundary recognition model that matches the system design operation domain and dynamic driving task is constructed， and a scenario design method based on the road traffic rule description model is proposed. The test verification of two automated driving systems shows that the road traffic rule scenario design method proposed in this paper can achieve high-coverage of road traffic rule compliance testing based on limited test cases， reduce invalid testing caused by mismatch between the system under test and the test scenario， and effectively identify situation that does not comply with road traffic rules. Figures and Tables | References | Related Articles | Metrics

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Research on Topology Modeling Method of Dynamic Driving Environment for Automated Driving Vehicle Simulation Xin Jia,Qiang Zhang,Zhiheng Zhang,Jinpeng Gao,Hsin Guan 2025, 47 (8):  1448-1458.  doi: 10.19562/j.chinasae.qcgc.2025.08.002 Abstract ( 234 )   HTML ( 12 )   PDF (2610KB) ( 90 )   Save To meet the requirements of simulation tools for automated driving system development， for the lack of complete and concise expression models and fast indexing methods for the driving related information in complex traffic scenarios， a dynamic driving environment modeling method based on topological psychological principles is proposed. With the environment representation model based on humanlike thinking patterns and the information storage and indexing methods based on humanlike memory patterns， effective organization， management， and rapid screening and extraction of environmental information required for driving decisions can be achieved. The model can be used for both traffic simulation and automated driving system. Two dynamic driving environment models of typical scenarios are established in VTD， and the effectiveness of the proposed method is verified. Figures and Tables | References | Related Articles | Metrics

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Quantum Key-Based UAV-Assisted Vehicle Authentication Scheme Teng Cheng,Ziang Cao,Xianli Xie,Xinyu Hong,Ze Yang,Qin Shi 2025, 47 (8):  1459-1467.  doi: 10.19562/j.chinasae.qcgc.2025.08.003 Abstract ( 274 )   HTML ( 8 )   PDF (3971KB) ( 64 )   Save With the rapid development of vehicle-road-cloud integration， Road Side Units （RSUs） have become increasingly significant in communication within Vehicular Ad-hoc Networks （VANETs）. When RSUs malfunction， unmanned aerial vehicles （UAVs）， leveraging their rapid deployment and flexibility， can swiftly serve as temporary mobile base stations. Given the open and dynamically changing network structure for communication between UAVs and vehicles， achieving secure communication and lightweight authentication is an urgent issue that needs to be addressed. Therefore， in this paper a hybrid encryption method based on quantum symmetric keys and Physical Unclonable Functions （PUFs） is proposed to ensure the security of information transmission and enable tracing of malicious UAVs or vehicle identities. Performance analysis shows that， compared to other lightweight authentication schemes， the proposed solution reduces computational overhead by approximately 5.82%-63.28% for vehicles and by around 43.98%-65.98% for UAVs. Figures and Tables | References | Related Articles | Metrics

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Prediction of Lane Change Intention Based on Driver's Cognitive-Making Space Qinyu Sun,Hang Zhou,Rui Fu,Chang Wang,Tao Huang,Junfeng Yang,Yunhao Wang 2025, 47 (8):  1468-1478.  doi: 10.19562/j.chinasae.qcgc.2025.08.004 Abstract ( 196 )   HTML ( 12 )   PDF (6663KB) ( 67 )   Save The key to human-machine collaboration in intelligent vehicles is to focus on people. Lane changing is one of the most basic driving tasks. Accurately and efficiently predicting the driver's lane changing intention is crucial to the development of humanized human-machine collaboration. Based on the theory of driver cognitive-making space， in this paper the driver's lane changing intention prediction experiment is designed. The relationship between vehicle operation data， driver's visual characteristics and driving scenes is analyzed， and a topological relationship diagram between the driver's gaze area and the driving scene is generated. Then the driver's lane changing intention prediction model dataset with different time windows is constructed. Based on the inverse residual deep separable convolution of the ConvNeXt （convolutional network） model， combined with the attention mechanism ECA （efficient channel attention）， ConvLSTM （convolutional long short term the memory） network and GCN （graph convolutional networks） figure structure， a predictive model of driver intention lane changing based on attention mechanism is built. The results show that when the time width of the data set is 3 s， the prediction accuracy of the model is the best， which is 91.15%， and the superior performance of the proposed driver lane change intention prediction model based on attention mechanism is fully verified by comparison and ablation experiments. Figures and Tables | References | Related Articles | Metrics

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Driver Behavior Recognition Method via MobileViT Model and Optical Flow Fusion Huizhi Xu,Jianzhao Zhang,Xiancai Jiang,Chengju Song 2025, 47 (8):  1479-1489.  doi: 10.19562/j.chinasae.qcgc.2025.08.005 Abstract ( 239 )   HTML ( 2 )   PDF (6552KB) ( 48 )   Save Based on the MobileViT algorithm， a novel driver behavior recognition model of Mse-MViT model is proposed in this paper， which integrates Convolutional Neural Networks （CNNs） with Transformers. The model uses the optical flow algorithm for recursive image processing， enabling the extraction of key frame sequences from the initial frame to the apex frame of a video clip to effectively capture driver motion information. A self-constructed Driver-vior dataset is introduced. Through multi-scale feature fusion， an SE attention mechanism， and dual-branch architecture， the model achieves comprehensive integration of motion cues with global and local image features. The experimental results show that the Mse-MViT model achieves a driver behavior recognition accuracy of 95.83%， exhibiting superior performance and robustness. Furthermore， comparative experiments conducted on the State Farm dataset show a 2.5% improvement in accuracy， validating the generalization capability and effectiveness of the proposed method. Figures and Tables | References | Related Articles | Metrics

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Research on Diffusion Reinforcement Learning Method for Vehicle Trajectory Tracking and Collision Avoidance of Autonomous Vehicles Junjie Zhao,Yinuo Wang,Jiang Wu,Sichao Wu,Changdi Zou,Hongda Wang,ShengboEben Li,Fei Ma,Jingliang Duan 2025, 47 (8):  1490-1500.  doi: 10.19562/j.chinasae.qcgc.2025.08.006 Abstract ( 307 )   HTML ( 11 )   PDF (3208KB) ( 133 )   Save The intelligence of autonomous vehicles is key to upgrading of the automotive industry， where trajectory tracking and collision avoidance technologies are crucial for ensuring vehicle safety. In this paper， for the problem of insufficient exploration of existing reinforcement learning control methods， a diffusion reinforcement learning algorithm is proposed. By combining diffusion models with reinforcement learning frameworks and replacing traditional policy networks with diffusion generative policy networks， introducing the multimodal distribution matching capability of diffusion models into reinforcement learning， and combining it with the distributional soft actor-critic algorithm， a diffusion distributional actor-critic algorithm （DDAC） is proposed. Simulation and real-vehicle experiments demonstrate that the proposed algorithm exhibits high exploration efficiency， with real vehicle lateral tracking error less than 0.03 m and velocity tracking error less than 0.05 m/s， verifying the superiority of the algorithm. Figures and Tables | References | Related Articles | Metrics

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Takeover Control Strategy Based on Human-Machine Game Yingkui Shi,Chuan Hu,Jianzhong Zou,Xi Zhang 2025, 47 (8):  1501-1512.  doi: 10.19562/j.chinasae.qcgc.2025.08.007 Abstract ( 237 )   HTML ( 9 )   PDF (3429KB) ( 46 )   Save Level 3 conditional autonomous driving systems can independently execute driving tasks in certain specific situation， allowing human drivers to get involved in tasks unrelated to driving. However， when the system sends out a takeover request， achieving a smooth transition from automatic to manual vehicle control during the driver's state recovery process is a key concern in current autonomous driving application. In this paper， for the takeover process of human driver in L3 autonomous driving， a human-machine non-zero-sum differential game framework is constructed for the authority transition based on the takeover behavior of human driver upon the system's takeover request and an event-triggered adaptive dynamic programming control algorithm is developed based on critic-only structure. By solving the driving strategies under the coupled control objectives of both players and designing exponential-trigonometric optimization algorithm-based flexible authority transfer strategy， a safe and stable switch between automatic and manual driving is achieved. The results show that the proposed takeover strategy has better obstacle avoidance performance and more stable vehicle motion than rigid takeover method. Figures and Tables | References | Related Articles | Metrics

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Intelligent Vehicle Decision for Roundabouts Based on Subjective Prior Reinforcement Learning Jian Wu,Yukang Shi,Bing Zhu,Jian Zhao,Zhicheng Chen 2025, 47 (8):  1513-1521.  doi: 10.19562/j.chinasae.qcgc.2025.08.008 Abstract ( 223 )   HTML ( 6 )   PDF (3336KB) ( 55 )   Save For the safety problems faced by intelligent vehicles in complex and highly interactive roundabout scenarios， a driving decision strategy based on Subjective Prior Deep Reinforcement Learning （SPDRL） is proposed. Firstly， a roundabout scenario model that includes the vehicle's longitudinal and lateral coupled action space， multi-scale information state space， and multi-objective reward function is constructed. Next， the Soft Actor-Critic （SAC） algorithm optimized with human preference reinforcement learning theory is used to design a driving decision strategy that considers the prior cognition of agent behavior risks. A self-learning subjective risk classifier， based on a multilayer perceptron， is applied to evaluate the prior cognition of agent behavioral risks and guide the driving decisions towards safer outcome. Finally， tests and verification are carried out using the CARLA simulation environment. The results show that the proposed strategy improves the safety performance of driving decisions by approximately 8.73% in roundabout scenarios compared to the standard SAC algorithm. Figures and Tables | References | Related Articles | Metrics

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Research on Predictive Energy-Saving Cruise Control for Electric Heavy Trucks Based on Cloud-Controlled Hierarchical Architecture Keke Wan,Fachao Jiang,Shuyan Li,Wei Zhong,Luyao Wang,Ao Zhang,Bolin Gao 2025, 47 (8):  1522-1533.  doi: 10.19562/j.chinasae.qcgc.2025.08.009 Abstract ( 231 )   HTML ( 8 )   PDF (7216KB) ( 77 )   Save With the continuous development of vehicle-road-cloud integration systems， cloud-controlled energy-saving technologies for intelligent connected vehicles have become a key focus for industrial application. However， the existing energy-saving control technologies for electric heavy-duty trucks exhibit two primary deficiencies. On the one hand， there is lack of cloud-controlled hierarchical architecture design specifically tailored for energy-efficient driving application. On the other hand， current energy-saving speed optimization research based on road gradient information does not fully consider the characteristics of electric heavy-duty truck power systems， such as regenerative braking energy recovery and coasting， constraining the energy-saving performance of such systems. To address these challenges， in this study a predictive energy-saving cruise control system for electric heavy-duty trucks based on the cloud-controlled hierarchical architecture is proposed. Firstly， the system architecture for energy-efficient driving application is designed based on the principles of cloud control system， and a rolling optimization control method for vehicle-cloud collaboration is proposed. Secondly， leveraging cloud-based gradient information and the energy consumption model of electric heavy-duty trucks， an energy-saving cruise control algorithm is developed that integrates economic speed， coasting， and regenerative braking energy recovery into coordinated planning. The algorithm constructs a state space under heterogeneous hierarchical densities and employs state-point approximation to solve the dynamic programming problem. Finally， the planning performance of the proposed algorithm is analyzed and validated under typical uphill and downhill conditions， demonstrating significant predictive energy-saving driving characteristics. Additionally， comparative simulation using real road gradient data is conducted against traditional energy-saving cruise control algorithms. The results show that the proposed algorithm achieves a 4.29% improvement in energy-saving efficiency by incorporating coasting operation， highlighting the potential of coasting in energy-saving control for electric heavy-duty trucks. A cloud-controlled hierarchical platform is constructed to comprehensively validate the system architecture and energy-saving performance. The results from 200 km of effective testing show that the proposed system achieves a maximum energy-saving efficiency of 8% compared to constant-speed cruise control， and an energy-saving rate of 1.62%-3.40% compared to manual driving. The above findings show that the cloud-controlled predictive energy-saving cruise control system has significant energy-saving potential and can comprehensively enhance the energy-efficient driving capabilities of vehicles and drivers， providing substantial value for industrial application. Figures and Tables | References | Related Articles | Metrics

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Panoramic Image Stitching Algorithm with Multi-frame-based View Transformation for Vehicle Heterogeneous Platform Lijuan Chen,Kaixin Wang,Lei Wu,Jiawei Lu,De Wu 2025, 47 (8):  1534-1545.  doi: 10.19562/j.chinasae.qcgc.2025.08.010 Abstract ( 172 )   HTML ( 5 )   PDF (8791KB) ( 32 )   Save Surround View System in ADAS （Advances Driver Assistance System） can effectively eliminate the driver's blind spots and reduce the accident rate. However， most existing vehicular panoramic imaging systems suffer from issues such as poor image quality， lack of smoothness， and low real-time performance. In this paper focusing on optimizing the image edge quality and algorithm real-time performance， a panoramic image-stitching algorithm based on multi-frame view transformation is proposed. Firstly， to solve the problem of reduced edge clarity and resolution of the fisheye camera correction image， the VDSR model is proposed to reconstruct the correction image. Secondly， a multi-frame-based view transformation algorithm is proposed， which accomplishes image perspective transformation while simultaneously performing image stitching， thereby reducing the algorithm's execution time. Additionally， motion compensation is applied to map adjacent frame fisheye images into the panoramic image， enhancing image quality. The vehicle experiments conducted on the in-vehicle heterogeneous platform show that compared with the single-frame view transformation， the panoramic images generated by the method proposed in this paper have improved by 2.27 dB and 0.022 in PSNR and SSIM indicators respectively， with the image output frame rate reaching 30 FPS， significantly enhancing the subjective visual quality of the panoramic images while ensuring real-time performance. Figures and Tables | References | Related Articles | Metrics

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Research on Thermal Management System of Electric Buses for Extreme Cold Regions Based on Thermal Energy Storage with Metallic Phase Change Material Peng Xie,Zhenhao Cai,Ruilin Luo,Jingfei Wang,Cheng Lin 2025, 47 (8):  1546-1558.  doi: 10.19562/j.chinasae.qcgc.2025.08.011 Abstract ( 207 )   HTML ( 15 )   PDF (6747KB) ( 55 )   Save For the problems faced by electric buses in cold regions during winter operation， such as severe reduction in driving range and excessively long preheating time， this research breaks through the limitation of traditional low-temperature thermal management technologies and proposes a vehicle thermal management solution based on thermal energy storage （TES） with metallic phase change material. A phase change TES device made of aluminum-silicon alloy for cold working conditions is designed. Then based on vehicle system simulation， the influence of capacity configuration of TES device on vehicle low-temperature performance is analyzed. The results indicate that the TES device has a mass energy density of 235 W·h/kg and a volume energy density of 429 W·h/L， whose energy storage cost is only 5%~20% of that of lithium-ion battery systems. Besides， at -40 ℃， adding a 110 kW·h TES device can increase the driving range by 115.5% and adding an 8 kW·h TES device can shorten the warm-up time by 65%. This study provides a new thermal management paradigm with both technical feasibility and economical rationality for the large-scale promotion of electric buses in cold regions， and is of great significance for promoting the comprehensive electrification of public transport in high latitude regions. Figures and Tables | References | Related Articles | Metrics

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Design and Path Tracking Method of Electrohydraulic All-Wheel Steering Systems for Four-Axle Heavy-Duty Unmanned Vehicles Yi Chen,Xiangyu He,Xiliang Zhou,Houlu Fan,Zhuoyu Wu 2025, 47 (8):  1559-1572.  doi: 10.19562/j.chinasae.qcgc.2025.08.012 Abstract ( 242 )   HTML ( 12 )   PDF (11075KB) ( 78 )   Save Four-axle heavy-duty unmanned vehicles （FHUVs） is a new type of special engineering vehicle characterized by multiple axles， high load bearing and large turning radii； thus， research on all-wheel steering system （AWSS） has become challenging. In this paper， an electrohydraulic AWSS for a new type of FHUV is designed， and path-tracking methods are researched. Firstly， a two-degree-of-freedom model （2-DOF） of a FHUV is constructed， and a new type of electrohydraulic AWSS is designed on the basis of analysis of the steering resistance and axle structure. Then， a path tracking method based on a preview control sliding mode controller （PSMC） is proposed to achieve precise control of the four-axle wheel angle of the vehicle. Finally， on the basis of different working conditions， the research content of this study is verified via a joint simulation model. In order to verify the stability and feasibility of the electrohydraulic steering system， an experimental platform is built， which demonstrates the good dynamic performance of the system. Figures and Tables | References | Related Articles | Metrics

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Research on the Forming Quality and Mechanical Properties of Flat-Surface-Overlapped Self-piercing Riveted Aluminum Alloy Joints Chao Wang,Wanyuan Yu,Aiguo Cheng,Zhicheng He,Tao Chen 2025, 47 (8):  1573-1587.  doi: 10.19562/j.chinasae.qcgc.2025.08.013 Abstract ( 195 )   HTML ( 7 )   PDF (10434KB) ( 36 )   Save A flat-surface-overlapped self-piercing riveting （FS-SPR） process is proposed to improve the joint quality in joining thick aluminum alloy sheets using self-piercing riveting （SPR）. Firstly， five different joints are fabricated to study the effect of riveting distance， overlap distance， and overlap surface type. Secondly， the mechanical properties and failure behavior under quasi-static and fatigue loads of FS-SPR joints are investigated through quasi-static and fatigue tests. Finite element simulation is conducted to study the forming mechanism of the FS-SPR process and explore the influence of process parameters on the forming quality of the FS-SPR joints with an inclined overlap surface. The results show that J1， J3， and J4 joints can form good mechanical interlock， while joint J2 fails to form mechanical interlock due to inadequate lower sheet thickness， and J5 joint experiences rivet bending due to large stack thickness. The simulation result shows that the overlap length needs to exceed 40 mm， the riveting position should be to the right of the middle， and the optimization of sheet thickness and rivet length is necessary to improve forming quality. Well-designed J1 and J3 joints outperform the traditional overlapped J5 joint. The peak force and energy absorption of J5 joint are 1.7% and 28.6% lower than J1 joint， and 10.7% and 6.4% lower than J3 joint， respectively. When subjected to the same load level， the fatigue life of J3 joint is on average 70% higher than that of J5 joint. Figures and Tables | References | Related Articles | Metrics

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Development and Validation of Injury Bionic Models for Occupants in Reclined Seating Posture for Large-Angle Seat Design Haiyan Li,Shengjiao Zhang,Yanxin Wang,Xinyu Zhang,Shihai Cui,Lijuan He,Wenle Lü 2025, 47 (8):  1588-1595.  doi: 10.19562/j.chinasae.qcgc.2025.08.014 Abstract ( 224 )   HTML ( 14 )   PDF (4624KB) ( 61 )   Save The rapid promotion of intelligent vehicles has expanded application scenarios for large-angle zero-gravity seats， presenting new challenges for the design of occupant restraint system. For the limitation of current crash test dummies in assessing occupant safety in reclined seating postures， based on the TUST IBMs F05 （5th percentile female） and TUST IBMs M50 （50th percentile male） occupant injury bionic models， a biomimetic model of occupant injury in reclined seating postures is developed in this study through posture adjustment reconstruction. The effectiveness of the model is verified by reconstructed cadaver tests. The test results indicate that the displacement-time curves of both models fall within the cadaveric response corridors. Additionally， the models accurately represent the thoracolumbar spine and pelvis geometry of the human body， while also exhibiting detailed soft tissue structures with high geometric realism and good mesh quality. With a high level of biofidelity， this model provides reliable baseline data for studying the injury mechanisms of reclined occupants in crashes， as well as for developing safety protection strategies and related regulations. Furthermore， it serves as an efficient computational tool for the development， safety assessment， and comfort evaluation of large-angle automotive seats. Figures and Tables | References | Related Articles | Metrics

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Research on Sound Quality Prediction of Special Vehicles Enhanced with GAN-FCNN Data Kun Qian,Xikang Du,Yanfu Wang,Jiying Duan,Ke Liu,Jing Tan,Zhenghua Shen,Jian Zhao 2025, 47 (8):  1596-1606.  doi: 10.19562/j.chinasae.qcgc.2025.08.015 Abstract ( 153 )   HTML ( 6 )   PDF (5452KB) ( 36 )   Save For the special vehicle sound quality prediction， the cost of collecting noise samples is high， and only a small number of sample sets can be obtained after sample processing， lacking sufficient noise samples， which affects the model accuracy during the training of various prediction models. In this paper， a GAN-FCNN network is established， and a four-layer fully connected layer is used to construct a generator and discriminator for adversarial training， and a pseudo-sample set is generated. The enhanced sample set is introduced into the LASSO linear regression model and the RF， BP and PSO optimization models respectively for regression prediction. Through verification， the prediction accuracy and performance of the models are improved. Compared with the traditional oversampling algorithm， the GAN-FCNN network has higher accuracy， which is more suitable for sample expansion in the establishment of special vehicle sound quality prediction model. Figures and Tables | References | Related Articles | Metrics

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Research on Active Control of Automotive Wind Noise Based on Improved Delayless Frequency Domain Algorithm Huijun Ji,Chihua Lu,Wan Chen,Zhien Liu,Ying Wang,Yongliang Wang,Menglei Sun 2025, 47 (8):  1607-1615.  doi: 10.19562/j.chinasae.qcgc.2025.08.016 Abstract ( 211 )   HTML ( 6 )   PDF (5727KB) ( 42 )   Save Wind noise is one of the main sources of interior noise in high-speed driving of new energy vehicles. Wind noise control by traditional structural design has problems such as difficulty in control and unclear noise reduction effect. In this article， an improved delayless frequency domain filtering active noise control algorithm based on secondary path equalization and frequency domain segmented variable step size method. This algorithm has the characteristics of lower computational complexity and less frequency dependence on system noise reduction performance compared to traditional filtering minimum mean square （FxLMS） algorithms， and can achieve good noise reduction effect over a wide frequency range. A dual channel simulation model of the algorithm is built in SIMULINK to simulate the noise reduction control effect of broadband wind noise， and a wind noise active control test bench is set up in a semi anechoic chamber for testing and verification. The results show that based on the wind noise data measured in real vehicles under high-speed conditions， the improved delayless frequency domain filtering algorithm proposed in this paper has significantly better noise reduction effect than the traditional FxLMS algorithm， with the noise reduction of about 9.42 dB（A） and 8.81 dB（A） at two target positions. This research result can provide new ideas and methods for active control and application of automotive wind noise. Figures and Tables | References | Related Articles | Metrics

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Research on the Application of Lightweight Aluminum-Ceramic Brake Discs in New Energy Vehicles Liuxu Cao,Henghu Wang,Shuhai Huo,Qiuer Chen,Lexiao Tao,Yiming Ma,Xiaofeng Yang,Xin Ding,Zhaoru Jiang,Chunxuan Liu,Qingsong Dai,Xiaoyong Zhang 2025, 47 (8):  1616-1626.  doi: 10.19562/j.chinasae.qcgc.2025.08.017 Abstract ( 214 )   HTML ( 11 )   PDF (6528KB) ( 83 )   Save In the context of carbon peaking and carbon neutrality， the demand for lightweight in new energy vehicles is increasingly urgent. As a critical component， the lightweight design of brake discs not only reduces unsprung weight but also enhances the vehicle energy efficiency and handling. Compared with the traditional cast iron brake discs， aluminum ceramic brake discs have obvious advantages in terms of comprehensive performance， and also show a certain degree of competitiveness in terms of production efficiency and cost-effectiveness， gradually becoming an alternative choice for the industry to focus on. However， there is limited research on bench tests and road tests of aluminum-ceramic brake discs under real working conditions and the performance evaluation of them in new energy vehicles is not comprehensive. Therefore， in this study the performance of aluminum-ceramic brake discs is comprehensively verified and analyzed for the first time through bench tests， road trials， and corrosion resistance tests. The results show that aluminum-ceramic brake discs outperform cast iron discs in terms of friction coefficient stability， with a nominal coefficient maintained between 0.3 and 0.35. Additionally， the aluminum-ceramic brake discs can withstand temperature up to 500 ℃， significantly higher than previously reported. During a long downhill test in the Heishan Valley， with energy recovery turned off， the maximum temperature of the aluminum-ceramic front and rear brake discs is 83 and 108 ℃ lower， respectively， than those of cast iron brake discs. The road trials and corrosion resistance tests show that aluminum-ceramic brake discs perform excellently in terms of noise， vibration， wear resistance， and corrosion resistance， with an estimated service life of up to 1 million kilometers， demonstrating good adaptability， reliability， and durability in extreme environment and road conditions. This research not only provides a more reliable braking system solution for new energy vehicles but also contributes to energy-saving and emission reduction， promoting sustainable development of the new energy vehicles. Figures and Tables | References | Related Articles | Metrics

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Influence of Carbon Fiber Blending on the Performance Regulation of Carbon Paper Zhilong Chang,Hui Chen,Jin Tao,Weiyu Cao,Zhigang Shen 2025, 47 (8):  1627-1633.  doi: 10.19562/j.chinasae.qcgc.2025.08.018 Abstract ( 164 )   HTML ( 6 )   PDF (2394KB) ( 24 )   Save The impact of doping different proportions （0， 5%， 10%， 25%， and 50%） of pitch-based carbon fiber on the properties of polyacrylonitrile （PAN）-based carbon paper is implored in this study. The microstructure， mechanical， electrical， gas transport， and electrochemical properties of the carbon paper are systematically characterized using scanning electron microscopy （SEM）， tensile strength measurements， in-plane resistivity measurements， air permeability tests， pore size distribution analysis， polarization curves， power density measurements， and electrochemical impedance spectroscopy （EIS）. The results show that doping pitch-based carbon fiber can significantly reduce the in-plane resistivity of the carbon paper， decreasing from 7.2 mΩ·cm to 2.0 mΩ·cm at a doping ratio of 50%. The tensile strength exhibits a trend of increasing followed by decreasing with increasing doping ratio， reaching the peak of 18.5 MPa at a 10% doping ratio， an approximately 176% increase compared to the undoped sample. The air permeability shows a trend of decreasing followed by increasing with increasing doping ratio， and the pore size distribution changes accordingly. Membrane electrode assembly （MEA） and single-cell tests indicate that the carbon paper doped with 5% pitch-based carbon fiber exhibits the best electrochemical performance， including lower electrochemical impedance and higher power density， attributed to its excellent water management capability. The findings reveal the regulatory effect of pitch-based carbon fiber doping on the properties of PAN-based carbon paper， providing a reference for optimizing the performance of gas diffusion layers of fuel cells. Figures and Tables | References | Related Articles | Metrics

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Research on Vehicle Structural Durability Based on User Data and the Correlation Between “Proving Ground and User” Lihui Zhao,Xiaoyu Xu,Shuo Weng,Dongjian Liu,Dongdong Zhang 2025, 47 (8):  1634-1645.  doi: 10.19562/j.chinasae.qcgc.2025.08.019 Abstract ( 188 )   HTML ( 7 )   PDF (8271KB) ( 81 )   Save Current structural durability specification of the proving ground fail to fully capture the complex load characteristics under the actual user operating conditions， which results in a discrepancy between the proving ground conditions and the actual user use. In this study the characteristics of user structural durability are analyzed based on user big data. Furthermore， the characteristics of the 'proving ground-user' correlation are studied. Firstly， the user data is subjected to preprocessing， and a multivariate time-sequence adaptive segmentation method is employed to construct user-operating segments. The characteristic parameters of operating segments are constructed from three perspectives： time domain， frequency domain and damage. Subsequently， linear and nonlinear methods are employed for feature parameter dimensionality reduction. K-Means clustering is employed to categorize the data into six typical operating conditions. For each typical operating condition， operating characteristics， damage contributions， and inter-city variations in user data are analyzed. Finally， a similarity analysis is conducted to map the relationship between the proving ground reinforced pavement and typical operating conditions. Differences in damage targets， power spectral densities， and extrapolated rain flow are analyzed， and the reasons for the differences are explored through the joint ‘damage level-speed’ distribution. The findings provide a foundation for selecting structural durability test conditions， defining life cycle objectives， and improving proving ground specifications under user conditions. Figures and Tables | References | Related Articles | Metrics