Table of Content

25 October 2025, Volume 47 Issue 10 Previous Issue   

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MPC Path Tracking Based on Adaptive Predictive Horizon Considering Active Actuators Characteristics Nan Xu,Zhuo Yin,Yuetao Zhang,Konghui Guo 2025, 47 (10):  1847-1860.  doi: 10.19562/j.chinasae.qcgc.2025.10.001 Abstract ( 461 )   HTML ( 55 )   PDF (7908KB) ( 256 )   Save To fully leverage the performance of multi-actuator chassis systems in the path tracking of autonomous vehicles， in this paper the stability analysis method is proposed which considers the influence of actuator characteristics on vehicle dynamics states. Based on this analysis， a model predictive control （MPC） based path tracking controller is designed， with a prediction horizon adaptively adjusted according to stability margins. For autonomous vehicles equipped with active front-wheel steering （AFS） and active rear-wheel steering （ARS）， the vehicle's dynamic state trends under actuator influence are first analyzed in the energy phase plane. A novel stability envelope boundary is defined based on the relationship between the dynamic state variation vector and the front and rear tire force saturation constraints， using Lyapunov's second method. Then， an adaptive prediction horizon calculation method is designed based on stability margin changes during trajectory tracking， and an MPC trajectory tracking controller is constructed using the nonlinear tire model， i.e.， UniTire-Ctrl. The co-simulation results from CarSim and Simulink demonstrate that the proposed stability envelope boundary more accurately estimates the vehicle stability boundary considering actuator characteristics， and the variable horizon MPC path tracking controller effectively balances path tracking accuracy with vehicle lateral stability. Figures and Tables | References | Related Articles | Metrics

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Vehicle Trajectory Prediction Method Based on Dynamic Attention and Goal-Guided Mechanism Yue Han,Yingfeng Cai,Long Chen,Xiaoqiang Sun,Hai Wang,Ze Liu,Zhongyu Rao 2025, 47 (10):  1861-1871.  doi: 10.19562/j.chinasae.qcgc.2025.10.002 Abstract ( 304 )   HTML ( 21 )   PDF (1964KB) ( 122 )   Save In complex traffic scenarios， reliably and effectively predicting the trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles. However， existing prediction methods often face challenges related to high computational overhead， making it difficult to achieve real-time and efficient trajectory prediction without sacrificing accuracy. Therefore， an innovative method called Dynamic Attention and Goal Guidance （DAGG） combining dynamic attention and goal guidance is proposed， which accurately captures the dynamics of changing scenes and identifies endpoint goals. To reduce redundant encoding and reasoning delay in continuous prediction， a local spatiotemporal reference framework is constructed that decouples intrinsic features from relative information between scene instances. Furthermore， an efficient and compact triple-factor attention fusion module is designed to aggregate local context features， capturing rich spatiotemporal background information. To achieve multimodal prediction and better utilize scene encoding， scene fusion features are injected into map information and a multimodal motion prediction decoding module is adopted to guide goal selection， capturing high-quality predicted goals while reducing the computational cost of goal-based trajectory generation. The validation results on the publicly available Argoverse dataset demonstrate that the proposed method achieves a minimum average displacement error （minADE） of 0.84 m and a minimum final displacement error （minFDE） of 1.26 m， significantly outperforming mainstream baseline models， which highlights its superior predictive capability in complex and dynamic scenarios. Figures and Tables | References | Related Articles | Metrics

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Unpaved Road Unevenness Recognition and Driving Risk Model Construction Based on the Improved Pointnet++ Wenguang Wu,Songjiang Qiu,Lin Hu,Xiaoqiang Zhang,Zenghua Qiu,Jiakai Wang 2025, 47 (10):  1872-1884.  doi: 10.19562/j.chinasae.qcgc.2025.10.003 Abstract ( 211 )   HTML ( 16 )   PDF (12285KB) ( 70 )   Save Potholes and bulges on unpaved road have a significant impact on vehicle driving safety. However， these uneven features cannot be simply regarded as obstacles or surface roughness， posing challenges for vehicle path planning and driving safety assessment in unpaved road environment. To address this， a LiDAR point cloud-based vehicle risk modeling method is proposed， considering the effect of surface unevenness， such as height/depth， area， and shape， on driving safety. Firstly， for the characteristics of pothole and bump features in unpaved road point cloud data， an improved PointNet++ model is proposed to enhance semantic segmentation performance and accurately identify uneven structures. Then， based on the semantic segmentation results and the defined categories of interest， three clustering algorithms are compared， and DBSCAN is identified as the most efficient method for clustering uneven features in unpaved road. Finally， to reflect the impact of various potholes and bumps on vehicle safety， terrain category coefficients and terrain density risks are introduced. A two-dimensional Gaussian function is used to construct a risk model for uneven terrain， enabling parametric representation of driving risks in unpaved road scenarios. The experimental and simulation results show that the improved PointNet++ model increases mIoU by 5.7%， and improves accuracy and recall by 5.3% and 6.4%， respectively. The proposed method achieves high-precision recognition of unpaved road unevenness and driving risk modeling， exhibiting strong robustness across different point cloud densities， road coverage， and road scenarios. Figures and Tables | References | Related Articles | Metrics

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Research on Motion Sickness Assessment Model Based on Multi-dimensional Parameter Fusion of Human and Vehicle by BiLSTM-Transformer Yupeng Lin,Li Ma,Tingting Lan,Rui Fu 2025, 47 (10):  1885-1894.  doi: 10.19562/j.chinasae.qcgc.2025.10.004 Abstract ( 210 )   HTML ( 3 )   PDF (2371KB) ( 70 )   Save To overcome the limitation of lack of accuracy of the existing methods in assessing motion sickness caused by a single vehicle motion condition， this paper aims to construct a multidimensional motion sickness assessment model. Firstly， a real-vehicle experiment containing four typical working conditions is designed in this paper， such as acceleration and deceleration， and synchronously three-axis acceleration and angular velocity data of the vehicle as well as the passengers' postures is collected to construct a multi-source dataset. Secondly， the empirical mode decomposition （EMD） detrending method is used to deal with the subjective evaluation scale of motion sickness， so as to eliminate the cumulative trend of exposure time， and reveal the influence mechanism of motion sickness in a single motion condition. Finally， a BiLSTM-Transformer hybrid model is constructed to capture the long sequence time-series dynamic features using BiLSTM， extract the global dependencies using the Transformer self-attention mechanism， and perform the multi-dimensional dataset combination comparison and ablation experiments. The human-vehicle fusion parameters combined with the BiLSTM-Transformer model achieve 92.6% accuracy on the test set， with a test loss as low as 0.23. The results show that the model can effectively assess the occurrence of motion sickness， with an improvement in accuracy of 4.3% and 9.9%， respectively， compared to the single BiLSTM and Transformer models， demonstrating the advantages of the human-vehicle motion parameter fusion and BiLSTM-Transformer model in the assessment of motion sickness. Figures and Tables | References | Related Articles | Metrics

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Vulnerable Road User Detection Method Based on Image Salient Feature Fusion Huanhuan Wang,Lisheng Jin,Ye Zhang,Xupeng Fu 2025, 47 (10):  1895-1904.  doi: 10.19562/j.chinasae.qcgc.2025.10.005 Abstract ( 178 )   HTML ( 7 )   PDF (5888KB) ( 62 )   Save For the challenges of target occlusion， feature conflict， and foreground-background blur in the detection of vulnerable road users in complex scenarios， a lightweight detection algorithm based on the fusion of image saliency features is proposed in this paper. Firstly， saliency features of the image are extracted using a reconstruction method， and these features are input into a convolutional neural network along with the color image. Next， a lightweight non-weight-sharing feature extraction fusion network is constructed to achieve deep feature fusion. The mixed attention mechanism is then introduced， and an efficient attention layer aggregation module is proposed to enhance the utilization efficiency of key features. Finally， training and testing are conducted on the constructed multi-class vulnerable road user dataset in complex scenarios. The results show that the proposed model efficiently and accurately detects vulnerable road users in complex traffic scenes， with an average precision of 94.3%， a precision of 94.6%， and a FPS of 23.25 Hz. Compared to the baseline network YOLOv7， the average precision is improved by 2.1%， and the precision is improved by 3.5%. Figures and Tables | References | Related Articles | Metrics

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Lane Detection for Complex Environment Based on Two-Branch Instance Segmentation Networks Ping Wang,Zhe Luo,Yunfei Zha,Yi Zhang,Youming Tang 2025, 47 (10):  1905-1913.  doi: 10.19562/j.chinasae.qcgc.2025.10.006 Abstract ( 156 )   HTML ( 3 )   PDF (2760KB) ( 41 )   Save For the problem of lack of lane line quantity recognition and insufficient accuracy of lane line segmentation in complex environment for self-driving cars， a lane line detection method with a two-branch instance segmentation network structure is proposed. Firstly， the method uses an encoding-decoding framework to improve the detail recovery ability and support multilevel feature fusion. Secondly， the fusion of feature pyramid network and advanced residual network improves the model's understanding of contextual information and deep semantics， which efficiently extracts the semantic features in complex lane lines. Then， the introduction of the SE module and the weighted least-squares fitting module strengthens the model's overall feature expression and generalization ability so as to improve the flexibility and accuracy of the model， and enhance the geometric shape prediction of lane lines without losing the computational performance of the model. Finally， the F1 of the algorithm experimentally tested on CULane and TuSimple datasets reaches 76.0% and 96.9%， respectively， and the experimental results show that the method can obtain good detection performance in complex environment such as light change， occlusion and road damage， and multiple lanes， and effectively improves the lane line detection accuracy. Figures and Tables | References | Related Articles | Metrics

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High-Fidelity Unsupervised Image Translation for Mismatched Semantic Data Zhuo Li,Libo Cao,Jiacai Liao,Haowei Cui,Yue Zhang 2025, 47 (10):  1914-1922.  doi: 10.19562/j.chinasae.qcgc.2025.10.007 Abstract ( 130 )   HTML ( 2 )   PDF (3561KB) ( 19 )   Save In the field of intelligent driving environment perception， image translation models often fail when there is a significant semantic mismatch between source and target domains， leading to semantic inversion and detail degradation. To address this challenge， in this paper a high-fidelity image translation method tailored for asymmetric domain data is proposed. Based on the diffusion-model generator structure， a multi-adaptive skip connection （MASC） module and a high-dimensional vector consistency loss （HVC loss） are proposed. The MASC module combines dynamic normalization and attention mechanism to adaptively process semantic and style information in skip connection， while the HVC loss constrains semantic mapping relationship in high-dimensional symbolic space. Compared to the optimal results， the proposed model reduces the FID and KID scores by 15.36 and 0.003 4 on RainSurface， and by 1.44 and 0.000 8 on public datasets， respectively. Figures and Tables | References | Related Articles | Metrics

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Design and Implementation of Offline Calibration for Vehicle-Mounted LiDAR with Common Field of View Weilong Fu,Jing Wang,Zhengchao Lei,Jun Zhao,Huan Xie 2025, 47 (10):  1923-1932.  doi: 10.19562/j.chinasae.qcgc.2025.10.008 Abstract ( 157 )   HTML ( 1 )   PDF (5331KB) ( 42 )   Save During the mass production of intelligent driving vehicles， there are inevitable errors in the assembly of lidar. In order to ensure that the lidar can provide accurate and reliable perception information， in this paper a calibration method and workstation construction scheme for the off-line calibration of lidar in intelligent driving vehicles is proposed. A calibration workstation is constructed， which includes a vehicle parking area and a target placement area with a target having spatial characteristics. The ground plane point cloud of the reference lidar is extracted in combination with the workstation， and a planar constraint is established with the point cloud of the design reference plane. At the same time， the constraint relationship is constructed by using the spatial position between the reference lidar and the target to complete the calibration of the reference lidar. A rigid body transformation calibration model between the reference lidar and the left and right lidars is established through target detection to complete the calibration of the left and right lidars. oint simulation calibration environment is established， and simulation experiments are designed and carried out to verify the effectiveness of the algorithm. Through the comparative analysis of real vehicle tests， in terms of stability and accuracy， the calibration results of the three-axis rotation and translation are similar to those of manual calibration. Compared with the scheme in the literature， the relative error rate of the rotation is significantly reduced， with the roll， pitch， and yaw reduced by approximately 11.7%， 3%， and 0.03% respectively， and the standard deviation reduced by 0.48， 0.68， and 0.11 respectively. In terms of calibration efficiency， it is approximately 16 times higher than manual calibration and approximately 8 times higher than the scheme in the literature. The results show that the scheme proposed in this paper has obvious advantages in terms of accuracy， stability， and efficiency， providing strong support and a solid guarantee for the off-line calibration of lidar. Figures and Tables | References | Related Articles | Metrics

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Research on Autonomous Emergency Steering of Vehicle Based on Offline Path Planning and iLQR Control Ningning Tu,An Cao,Mu Yuan,Mingyang Hou,Shengping Liang 2025, 47 (10):  1933-1941.  doi: 10.19562/j.chinasae.qcgc.2025.10.009 Abstract ( 181 )   HTML ( 2 )   PDF (1976KB) ( 41 )   Save In the study， an offline Bezier path is generated based on the shortest escaping time for AES （Autonomous Emergency Steering）. An iLQR（iterative Linear Quadratic Regulator） scheme is introduced for trajectory tracking and control. Simulation is applied to validate the proposed method. It is found that compared to quintic polynomial， “brachistochrone curve” is more desirable for the emergency scene like AES， which achieves the relatively fastest collision avoidance. Combined with offline planning mode， the computation cost is acceptable， and steering performance is completely exploited， with much less parameter calibration. For AES， the control error is characterized by abruptness， and a large control input is always requested. The nonlinearity of system is essential to be taken into consideration. Through an iterative procedure in predictive horizon， the optimal control input is obtained， which satisfies the request of fast response during path track of AES. Figures and Tables | References | Related Articles | Metrics

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Research on Optimization and Suppression of Torque Ripple of Vehicle Motor Based on Small Sample WPRBF-MEVO Long Chen,Feihong Li,Xiaobin Chen,Chuanhao Lu,Xiaonan Zhao,Qiaobin Liu 2025, 47 (10):  1942-1952.  doi: 10.19562/j.chinasae.qcgc.2025.10.010 Abstract ( 152 )   HTML ( 6 )   PDF (5923KB) ( 20 )   Save The torque ripple of the drive motor directly affects the NVH performance of the entire vehicle. To address the optimization and suppression challenge of this issue， a collaborative optimization framework integrating the Weighted Average and Polynomial Augmented Radial Basis Function （WPRBF） surrogate model and the Multi- Objective Energy Valley Optimizer （MEVO） algorithm is proposed. Firstly， a parametric finite element model of the motor is established and verified through bench tests. Secondly， Latin hypercube sampling is employed for experimental design to obtain samples， and the WPRBF method is proposed to construct a high-precision surrogate model. Finally， the MEVO algorithm is used for multi-objective optimization design， and the entropy weight-fuzzy set theory comprehensive decision-making mechanism is introduced to obtain the Pareto front optimal solution. The results show that： （1） Under the same modeling accuracy， the WPRBF model requires approximately 40% fewer samples than the traditional KRG surrogate model； （2） After optimization， the mean value of the motor torque output increases by 6.83%， with the torque ripple coefficient decreasing by 20.00%， and the peak value of cogging torque reduced by 23.80%. This verifies the effectiveness of the method proposed in this paper. Figures and Tables | References | Related Articles | Metrics

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Investigation on the Performance of PEMFC with Dolphin-Inspired Biomimetic Flow Field Shuanyang Zhang,Hongtao Xu,Guobin Zhang,Xiaoping Chen,Quan Yuan,Guanyi Chen 2025, 47 (10):  1953-1962.  doi: 10.19562/j.chinasae.qcgc.2025.10.011 Abstract ( 172 )   HTML ( 4 )   PDF (5063KB) ( 33 )   Save In order to improve the output performance and water and thermal management capabilities of proton exchange membrane fuel cells （PEMFCs）， inspired by the drag-reducing shape of dolphins， a dolphin-shaped blockage flow field （DSB-FF） is designed in this paper. A non-isothermal three-dimensional model of the biomimetic flow field is developed and analyzed using Fluent. The simulation results show that the polarization curves of DSB-FF are almost identical to those of the triangular blockage flow field （TB-FF）. Although the peak power density of TB-FF is 2.06% and 0.61% higher than that of the parallel flow field （PFF） and DSB-FF， respectively， its high pressure drop of 27.978 Pa reduces the net output power to 0.720 W. In contrast， DSB-FF， with its superior drag-reducing design， achieves the highest net output power of 0.986 W， effectively balancing power density and pressure drop while maintaining a higher oxygen molar concentration across most regions. Additionally， compared to PFF， DSB-FF accelerates the flow velocity of reactive gases within the flow channels， reducing the drainage time by approximately 5 ms. Among the flow fields， the decreasing contact angle flow field demonstrates the best drainage performance with the shortest drainage time of 11 ms， outperforming the mean and increasing contact angle flow fields. Finally， the DSB-FF equipped with cooling channels effectively reduces the high-temperature regions， exhibiting superior thermal management capabilities compared to PFF. Figures and Tables | References | Related Articles | Metrics

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State of Health Estimation for Sodium-Ion Batteries Based on Features Fusion of Incremental Capacity and Relaxation Voltage Chenyan Gu,Jun Peng,Hui Wang,Xuan Zhao,Jian Ma,Jielun Meng,Siqian Yan 2025, 47 (10):  1963-1972.  doi: 10.19562/j.chinasae.qcgc.2025.10.012 Abstract ( 178 )   HTML ( 1 )   PDF (5358KB) ( 31 )   Save The State of Health （SOH）， a critical metric for evaluating battery aging and performance degradation， requires accurate estimation to ensure the safe operation and lifespan management of battery systems. Compared to the well-established lithium-ion battery systems， the aging mechanism and capacity degradation behavior of sodium-ion batteries remains insufficiently understood. In this study， a SOH estimation method for sodium-ion batteries is proposed by fusing incremental capacity （IC） and relaxation voltage （RV） features. The IC curves are employed to analyze phase transition dynamics during charge/discharge processes， while RV features are utilized to characterize electrode polarization recovery patterns during resting periods， thereby comprehensively revealing multi-dimensional aging mechanism. A feature fusion model is developed to enhance the sensitivity and noise immunity of health indicators. By leveraging machine learning algorithms， the mapping relationship between IC/RV-derived features and SOH is established， constructing an LSTM-Attention （Long Short-Term Memory network integrated with an attention mechanism） based estimation model. The experimental results show that the proposed method achieves superior SOH estimation accuracy （RMSE<0.51%，MAE<0.40%） compared to single-feature approaches， providing a robust solution for real-time health monitoring and industrial deployment of sodium-ion batteries. Figures and Tables | References | Related Articles | Metrics

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Robust Model Predictive Anti-Disturbance Control for Active Suspension System Based on Output Feedback Observer Bing Zhu,Haiqiao Li,Jian Zhao,Zhicheng Chen,Jie Hou,Shiwei Wang,Shuwei Ding 2025, 47 (10):  1973-1984.  doi: 10.19562/j.chinasae.qcgc.2025.10.013 Abstract ( 190 )   HTML ( 0 )   PDF (3169KB) ( 54 )   Save To overcome the nonlinear disturbance such as actuator delay and external random input in Active Suspension System （ASS）， and reduce its dependence on high-cost sensors， an Output Feedback Observer-based Robust Model Predictive Anti-disturbance Control （OB-RMPAC） strategy is proposed. Firstly， the dynamics model of half-car active suspension system is established considering the nonlinear disturbance of actuator delay. Secondly， an output feedback observer which can accurately estimate the state of ASS under external nonlinear input disturbance is constructed by applying the quadratic boundedness condition. Thirdly， the robust model predictive anti-disturbance control strategy that satisfies multiple constraints of ASS is designed by combining invariant set theory， linear matrix inequality and convex optimization techniques. Finally， the recursive feasibility and robust stability of the closed-loop predictive control system are analyzed and proved. The test results show that the OB-RMPAC strategy proposed in this paper can significantly improve the dynamic performance of vehicle equipped with ASS on the basis of accurate estimation of system state. Figures and Tables | References | Related Articles | Metrics

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Research on Collision Analysis and Accuracy Validation of Aluminum Alloy Bumper Based on Chaboche Combined Hardening Model Deyu Kong,Dejian Meng,Yunkai Gao 2025, 47 (10):  1985-1993.  doi: 10.19562/j.chinasae.qcgc.2025.10.014 Abstract ( 155 )   HTML ( 4 )   PDF (5145KB) ( 29 )   Save The purpose of this study is to investigate the performance of Chaboche combined hardening model in crash simulation of aluminum alloy bumper beam. Therefore， in this study the mechanical properties of 6082-T6 aluminum alloys are systematically analyzed through quasi-static/dynamic tensile tests and cyclic three-point bending tests， and parameter calibration is conducted for the Johnson-Cook model and the Chaboche combined hardening model. Based on a 100% overlap sled frontal impact test， a finite element model of the aluminum alloy bumper is constructed， and the differences in collision response prediction between different material models are compared. The results show that both models can effectively simulate the structural deformation characteristics. However， the Chaboche combined hardening model shows a 6.4% increase in the predicted maximum plastic deformation compared to the Johnson-Cook model， which is closer to the experimental results. Regarding acceleration response prediction， the Johnson-Cook model exhibits a significant amplitude error when compared to the experimental data， while the predicted value of Chaboche combined hardening model is in good agreement with the experimental value， with an error of less than 10%. The study shows that introducing the kinematic hardening model to account for the complex loading conditions in actual collision problems is meaningful. Figures and Tables | References | Related Articles | Metrics

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Development and Validation of a Digital Evaluation Model with Anthropometry of 95th Percentile Chinese Male Lijuan He,Kangwei Zhao,Haiyan Li,Shihai Cui,Wenle Lü 2025, 47 (10):  1994-2003.  doi: 10.19562/j.chinasae.qcgc.2025.10.015 Abstract ( 210 )   HTML ( 5 )   PDF (5084KB) ( 56 )   Save As a key tool for automotive active and passive safety integration research， the biorealism and human characterization fitness of digital assessment models directly affect the reliability of virtual assessment. Currently， there is limitation in the study of the kinematic and biomechanical responses of large male occupants in automobile crashes. In this study， a digital assessment model （TUST IBMs M95-O） with high biofidelity for the 95th percentile of Chinese physical characteristics is proposed based on the 95th percentile male physical characteristics data from the China National Institute of Standardization and the CT medical images of volunteers. The validity of the model is evaluated from multiple perspectives by reconstructing cadaveric tests in five regions of the human body， including the shoulder， chest， abdomen， pelvis， and knee joints. It is also applied to compare with existing 5th percentile female （TUST IBMs F05-O） and 50th percentile male （TUST IBMs M50-O） human finite element models for low speed rear impact tests. The results show that the predicted results of the 15 sets of simulation tests have the same trend with the cadaver test data， all within the corresponding cadaver test channel， and the average difference between contact force and compression is within 10%， which verifies the validity of the model. In the low speed rear impact test， all three somatic models are consistent with the experimental kinematic responses of the volunteers. This model， as a human biomechanical model of the Chinese body signs series， fills the technical gap of injury prediction for large-sized male occupants， which can provide technical support for the establishment of virtual evaluation system of automobile crash safety. Figures and Tables | References | Related Articles | Metrics

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A Robust Sound Field Zoning Control Method Based on Error Probability Model for a Car Cabin Zexi Tang,Xiangning Liao,Fusheng Bai,Hao Luo,Jie Li 2025, 47 (10):  2004-2015.  doi: 10.19562/j.chinasae.qcgc.2025.10.016 Abstract ( 134 )   HTML ( 2 )   PDF (3613KB) ( 39 )   Save The Personal Sound Zone （PSZ） technology in automotive cabins is a spatial sound field reproduction technique that uses an array of speakers to create independent sound zones within the cabin. When designing the speaker control signals， it is essential to accurately obtain the acoustic transfer functions （ATFs） between the speakers and the control points within the targeted area. However， in practice， various interferences prevent the ideal ATFs from being obtained， resulting in degraded sound field reconstruction performance. Traditional robust control methods typically add regularization terms to the optimization problem， but these methods face challenges in selecting appropriate regularization parameters. To address the problem， a robust ACC-LD method based on a probabilistic model of transfer function errors is proposed in this paper. This approach fits the errors of the ATFs using the Gaussian mixture model， and the Expectation-Maximization （EM） algorithm is employed to train the model and obtain the distribution parameters. The errors are then incorporated into the ACC-LD optimization problem and solved by taking the expectation， thereby avoiding the parameter tuning issues inherent in regularization methods. A comparative study in the simulated automotive cabin environment shows that the proposed method outperforms traditional regularized robust control methods in terms of contrast between light and dark area as well as the consistency in the bright zones. Figures and Tables | References | Related Articles | Metrics

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Sensitive Area of Tire Wear Signal Characteristics and Wear Estimation Method for Intelligent Tire Guolin Wang,Xin Wang,Zhecheng Jin,Xiangliang Li,Yu Zhang 2025, 47 (10):  2016-2026.  doi: 10.19562/j.chinasae.qcgc.2025.10.017 Abstract ( 235 )   HTML ( 5 )   PDF (4287KB) ( 83 )   Save Tire wear not only affects vehicle driving safety， but also has an important impact on the optimization of tire physical model parameters. In this paper， a tire wear state estimation method that can be applied to strain type intelligent tires is proposed. Firstly， the circumferential strain of the inner liner of the moving tire is obtained by using finite element technology and the impact mechanism of wear on it is analyzed. Four feature indicators closely related to tire wear are proposed. Then， based on the global sensitivity indicator theory， the sensitivity of these wear features to tire using conditions （wear， tire pressure， vehicle speed， and load） and the inner liner sensitive area are explored. The results show that the first derivative of the circumferential strain at the center point of the tire inner liner is the most sensitive to wear features， while the circumferential strain at 17-27 mm on either side of the center point is the most sensitive to wear features， which can be used to guide the sensor installation position. Finally， the Gaussian process regression is used to develop the wear state estimation model， and the average RMSE of the model estimation results considering the tire use conditions is only 0.166 mm. This method not only ensures the estimation accuracy， but also makes full use of the established data resources during the vehicle driving process， ensuring effective monitoring and management of tire wear state. Figures and Tables | References | Related Articles | Metrics

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Study on Improvement of Human Thermal Comfort by Distributed Air Supply in Special-Purpose Vehicle Occupant Compartments by Simulation Ji’an Zhou,Liang Ling,Tingting Jiao,Wenjie Ji,Wei Du 2025, 47 (10):  2027-2036.  doi: 10.19562/j.chinasae.qcgc.2025.10.018 Abstract ( 186 )   HTML ( 7 )   PDF (4515KB) ( 53 )   Save Occupant compartments of special-purpose vehicles undertaking specialized missions frequently face challenges of uneven thermal distribution. To optimize the thermal environment while enhancing human thermal comfort and operational efficiency， in this study a CFD model for a special-purpose vehicle cabin is established. The validity of the CFD model is verified through simplified cooling tests in an environmental chamber. Three human thermal comfort metrics， average occupant skin temperature， head-to-foot temperature difference， and average PMV （predicted mean vote）， are employed as evaluation criteria. Simulation is conducted on three distributed air supply configurations with a single return vent under cooling mode： single-supply/single-return， triple-supply/single-return， and quintuple-supply/single-return. The results show that the quintuple-supply/single-return configuration with personalized air supply vents positioned 100 mm above each occupant’s head significantly enhances cooling effectiveness， reducing the maximum temperature difference in average skin temperature by 2.9 ℃， improving temperature uniformity within the occupant compartment （with head-to-foot temperature difference decreased by 0.3 ℃）， and markedly improving human thermal comfort （with PMV reduced by 1.9）. The findings provide critical insights for optimizing air supply/return systems in special-purpose vehicle occupant compartments， supporting the design of healthy and comfortable high-quality cabin environment. Figures and Tables | References | Related Articles | Metrics

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Study on the Performance of an Innovative Indirect Thermal Management System for Automotive Application Using R290 Refrigerant Jingyang Hua,Binbin Yu,Li Yu,Yunlong Zhang,Mengdi Xu,Junye Shi,Jiangping Chen 2025, 47 (10):  2037-2048.  doi: 10.19562/j.chinasae.qcgc.2025.10.019 Abstract ( 189 )   HTML ( 10 )   PDF (5377KB) ( 70 )   Save With the increasing application of the environmentally friendly refrigerant R290 in automotive thermal management systems， enhancing system performance and energy efficiency has become a key research focus. In this study an innovative R290-based indirect thermal management dual secondary loop system using the dual-core counterflow series heat exchange architecture is proposed. The system’s boundary performance is tested through experiments and a simulation model of the electric vehicle thermal management system is established. The performance differences between the system and the single-core and dual core parallel flow series heat exchange solutions are compared and analyzed. The results show that， under extreme summer cooling conditions （49 °C）， the counterflow system increases cooling capacity by 11.62 % and achieves a COP of 1.67 compared to the single-core solution. In extreme winter heating conditions （-20 ℃）， the system’s COP improves by over 9.5 % compared to the parallel flow arrangement. This research provides valuable technical support for the efficient application of R290 in vehicle thermal management systems. Figures and Tables | References | Related Articles | Metrics