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25 October 2024, Volume 46 Issue 10 Previous Issue    Next Issue

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Research on a Property-Based Dynamic Model of the Solid Axle and Coupled Suspension for Commercial Vehicles Li Li,Wei Huang,Yue Gao,Lei Sun,Baoli Zhu,Xin Guan,Jun Zhan,Le Jiang,Chunguang Duan,Chenxue Cui,Wei Wang 2024, 46 (10):  1723-1732.  doi: 10.19562/j.chinasae.qcgc.2024.10.001 Abstract ( 415 )   HTML ( 59 )   PDF (5866KB) ( 415 )   Save The axle and suspension are critical components of vehicles. To achieve real-time simulation of the solid axle and various suspension structures of a commercial vehicle， the property-based modeling technical route is adopted in this paper. The axle's movement is decoupled into motion kinematics and ride dynamics， while the suspension characteristics are divided into coupled carrying characteristics， RC/PC guiding characteristics， and coupled K&C kinematic characteristics. Innovatively considering the pitch dynamic effect of the axle， the nonlinear dynamic coupling relationship of suspension between axles， and the K&C coupling relationship of suspension between axles， a dynamic model for commercial vehicles is developed to trigger the negative phenomenon of brake vibration. Additionally， a K&C testing method for the coupled suspension and a method for model parameter identification are proposed. Finally， the accuracy of the model is validated at the system level by comparing K&C test data with TruckSim model results. Inputting vehicle parameters into the UniTruck software for simulation and comparing the results with TruckSim simulation， the model’s effectiveness is verified at the vehicle level. Figures and Tables | References | Related Articles | Metrics

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Chassis Coordinated Control for Lateral Stability of Four-in-Wheel-Motor-Drive Vehicles Zhihao Yu,Rongkang Luo,Peibao Wu,Zhichao Hou 2024, 46 (10):  1733-1743.  doi: 10.19562/j.chinasae.qcgc.2024.10.002 Abstract ( 323 )   HTML ( 32 )   PDF (3870KB) ( 339 )   Save To improve the lateral stability of four-in-wheel-motor-drive vehicles， in this paper a chassis coordinated control strategy that integrates torque coordination and active rear steering is proposed. The strategy aims to track the ideal yaw rate and sideslip angle while effectively reduce the body roll motion. Based on the characteristics of the vertical reaction force generated by the in-wheel motor， the decoupled control for the longitudinal， yaw and roll motions of the vehicles is designed based on torque coordination. To decrease the effect of ignored nonlinearity and uncertainty in modeling lateral dynamics on the control performance， a disturbance observer-based model predictive control for chassis cooperative control is designed to estimate and compensate the nonlinearity and uncertainty. To verify the effectiveness of the proposed method， a hardware-in-the-loop test is conducted for the double lane change maneuver. The results show that the proposed control strategy can improve the lateral stability and reduce the roll motion of the vehicle body. Furthermore， compared to the control without disturbance compensation， the disturbance observer-based control reduces the tracking errors of the desired yaw rate and sideslip angle by 56.9% and 27.3%， and the body roll angle and roll rate by 8.9% and 12.5%， respectively. Figures and Tables | References | Related Articles | Metrics

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Research on DOB-Based Switching Control Algorithm for Active Suspension System Xiaokai Chen,Feng Chen,Xiang Liu,Hongyu Liu,Xiaoyu Wang 2024, 46 (10):  1744-1754.  doi: 10.19562/j.chinasae.qcgc.2024.10.003 Abstract ( 199 )   HTML ( 10 )   PDF (5052KB) ( 414 )   Save Suspension control requires good balance between ride comfort and driving stability， while considering system uncertainties， which is a complex task. In this paper， a disturbance observer-based suboptimal-nonsingular terminal sliding mode switching control algorithm （DOB-SNTSM） is proposed， with considerations of suspension dynamic performance indicators， algorithm robustness， and cost factors. Firstly， using spring mass acceleration information as input and by Kalman filter design， effective estimation of suspension deflection and spring mass velocity is achieved. Subsequently， a disturbance observer is devised to estimate uncertainties within the suspension system， with the disturbance estimation serving as feedforward compensation. Next， based on the sliding mode surface function， a suboptimal-nonsingular terminal sliding mode switching control algorithm is proposed， integrating with the feedforward compensation from the disturbance observer to formulate a novel active suspension control strategy. Finally， simulation and bench tests are conducted on both convex road surfaces and smooth random road surfaces. The results show that the introduction of disturbance observers can significantly improve the ride comfort index of the suspension. Compared to the SNTSM algorithm with the classical sky-hook control， the ideal state LQR method and without disturbance observer， the new algorithm not only effectively balances various suspension performance indicators but also achieves control effect close to the ideal state LQR using solely spring mass acceleration information. Additionally， the controller switching scheme significantly enhances algorithm robustness. Figures and Tables | References | Related Articles | Metrics

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Dynamic Braking Allocation Strategy for Turning-Braking Maneuver Tong Wu,Jing Rong,Junnian Wang,Wen Sun,Liang Chu,Linhe Ge 2024, 46 (10):  1755-1765.  doi: 10.19562/j.chinasae.qcgc.2024.10.004 Abstract ( 251 )   HTML ( 14 )   PDF (4476KB) ( 546 )   Save The vehicle dynamics during turning-braking maneuver are more complex than those on the straight lanes due to tire sideslip， load transfer and other factors. In depth investigation of the braking allocation strategy for enhancing the vehicle tracking performance in this maneuver is of great significance for driving safety. In this regard， a dynamic braking allocation strategy within electro-mechanical brake （EMB） is further investigated in this study. Firstly， the 2-DOF-vehicle dynamics model is taken as a reference， and the minimum lateral force requirements for stable driving of the front and rear axles are solved based on the model predictive control （MPC） algorithm. Then， the maximum longitudinal force available for braking each wheel is obtained by solving the friction circle online. Moreover， the braking allocation ratios are calculated according to the obtained maximum longitudinal force to realize the optimal braking allocation， The simulation and test results show that the proposed strategy enhances the vehicle tracking performance in turning-braking by dynamically adjust the braking force allocation ratios according to the driving conditions， load status and road adhesion conditions of the vehicle. Figures and Tables | References | Related Articles | Metrics

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Coordination Control of 4WS and DYC for in Wheel Motor Driven Electric Vehicle Haichuan Zhang,Shu Wang,Xuan Zhao,Chenyu Zhou,Cangyan Guo,Meng Zhou 2024, 46 (10):  1766-1779.  doi: 10.19562/j.chinasae.qcgc.2024.10.005 Abstract ( 228 )   HTML ( 16 )   PDF (7164KB) ( 249 )   Save In order to improve the path tracking ability and handling stability of in wheel motor driven electric vehicles， a novel coordination control strategy for active four-wheel steering （4WS） and direct yaw moment control （DYC） is proposed. Firstly， considering the path tracking performance and handling stability of vehicles， a shared steering model is established and on this basis， the 4WS control strategy based on non-cooperative Nash game theory is proposed. Secondly， in order to improve the lateral stability of the vehicle under extreme conditions， the vehicle state is divided into stable， transitional， and unstable regions based on the phase plane of the center of mass sideslip angle， and the DYC controller is established in each region. Then， in order to achieve coordinated control of rear wheel steering and direct yaw moment， the coordination controller based on fuzzy neural network is established between ARS and DYC. Finally， the CarSim/Simulink co-simulation platform and Hardware-in-the Loop （HIL） platform are used to conduct experimental verification under dual line shifting conditions. The research results show that the proposed control strategy can effectively improve the path tracking precision and handling stability of the vehicle under extreme driving conditions. Figures and Tables | References | Related Articles | Metrics

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Trajectory Planning and Control of Autonomous Vehicle Under Extreme Conditions Based on Autonomous Drift Shaobo Lu,Lingfeng Dai,Chenhui Wang,Bingjun Liu,Zhigang Chu,Wenke Xie 2024, 46 (10):  1780-1789.  doi: 10.19562/j.chinasae.qcgc.2024.10.006 Abstract ( 280 )   HTML ( 27 )   PDF (2572KB) ( 274 )   Save To consider both stability and trajectory tracking performance of autonomous vehicles operating in extreme conditions， a trajectory planning and control method based on autonomous drift is proposed. A neural network tire dynamics model is designed based on neural network to improve the accuracy of the traditional magic tire formulation. In order to further expand the stability boundaries under the extreme working conditions of autonomous vehicles， the drift stability boundaries are designed based on the tire saturation and maximum sideslip characteristics combined with the center-of-mass lateral deflection angle-transverse swing angular velocity phase plane constraints during drift， and the nonlinear model predictive control （NMPC） is used to plan a safe drift trajectory within a wider stability range， and the drift tracking control is carried out for the planned trajectory. The results of the joint simulation of Simulink/CarSim show that the method can fully utilize the advantages of drift motion to ensure that the vehicle does not go out of control under extreme working conditions， while accurately tracking the desired trajectory. Figures and Tables | References | Related Articles | Metrics

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A Cascade Control Scheme for Path Tracking with Model Predictive Path Integral and Output Regulator Hang Wan,Shida Nie,Hui Liu,Fawang Zhang,Changle Xiang,Lijin Han 2024, 46 (10):  1790-1803.  doi: 10.19562/j.chinasae.qcgc.2024.10.007 Abstract ( 135 )   HTML ( 4 )   PDF (4042KB) ( 272 )   Save Due to the limitation of the computing power of the vehicle platform， there is an irreconcilable contradiction between the long-term control/prediction time domain and the short-term control step. In this paper， a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently， the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a more accurate dynamic model and a shorter control cycle. In addition， model predictive path integral （MPPI） strategy is used to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. Finally， a model predictive output regulator is proposed to solve the underactuated control problem in vehicle lateral dynamics and reduce steady-state errors in yaw angel. Theoretical analysis and simulation results show that the proposed method enhances computing efficiency， improves the parameters adaptability and steering smoothness and reduces the lateral jerk by an average of 50% in all driving scenarios. Figures and Tables | References | Related Articles | Metrics

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Path Tracking Control of Intelligent Vehicle Based on Learning Model Predictive Control Hongmao Qin,Shu Jiang,Tiantian Zhang,Heping Xie,Yougang Bian,Yang Li 2024, 46 (10):  1804-1815.  doi: 10.19562/j.chinasae.qcgc.2024.10.008 Abstract ( 227 )   HTML ( 20 )   PDF (2450KB) ( 469 )   Save Path tracking control is a key technology for intelligent vehicles. However， the existing vehicle tracking control methods mostly rely on more accurate vehicle control models， while actual vehicle control systems mostly have modeling errors， parameter perturbations and external disturbances， which significantly affect path tracking control accuracy. In this paper， a learning path tracking control method for intelligent vehicles considering unmodeled dynamics of vehicles is proposed. Firstly， a nominal model of the vehicle is established and a linear prediction model is used to approximate the compensation for the unmodeled dynamics of the vehicle to improve the accuracy of the vehicle model. Then， learning and updating of the parameters of the unmodeled dynamics are realized based on the principle of Extended Kalman Filtering. Next， learning Model Predictive Controller （LMPC） considering the unmodeled dynamics of the system is established. Finally， the effectiveness of the proposed method in improving the path tracking accuracy is verified by designing a joint simulation test with Carsim and Matlab/Simulink for multiple operating conditions and multiple groups. Figures and Tables | References | Related Articles | Metrics

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Model Predictive Anti-disturbance Control for Longitudinal Motion of Intelligent Vehicles Under Multi-source Disturbances Zhong Zhang,Xiaojian Wu,Huihua Jiang,Chao Zhang,Yukang Wan 2024, 46 (10):  1816-1828.  doi: 10.19562/j.chinasae.qcgc.2024.10.009 Abstract ( 183 )   HTML ( 13 )   PDF (8147KB) ( 87 )   Save The precision of speed tracking in the longitudinal motion control of intelligent vehicles is affected by multiple sources of disturbances， such as model mismatch and changes in external environments. In this paper， a longitudinal motion anti-disturbance control method that combines disturbance observation and Model Predictive Control （MPC） algorithm is accordingly proposed. Firstly， the relationship between the longitudinal acceleration of the vehicle and various forces is analyzed according to the longitudinal dynamics model of the vehicle， and then it is simplified into a particle motion model with multiple sources of disturbance and a model predictive controller is designed as the upper controller. Secondly， for the internal unmodeled dynamic disturbances and external random disturbances， a linear extended state observer （LESO） is designed to perform real-time estimation and compensation is implemented through a feedforward loop. The closed-loop stability of MPC and the convergence of LESO are analyzed， and finally a model predictive optimal regulation control law of disturbance compensation and state feedback is formed. Furthermore， in order to ensure efficient execution of the control strategy， a first-order anti-disturbance controller is proposed as the lower controller to convert the desired acceleration into engine torque， thereby realizing closed-loop control of the vehicle speed. Finally， the algorithm is deployed on a in-vehicle Microcontroller Unit （MCU） and tested on a real vehicle under multi-speeds and road conditions. The results show that the proposed strategy can quickly and accurately track the target vehicle speed， with excellent anti-disturbance ability. Figures and Tables | References | Related Articles | Metrics

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Model Predictive Control with Adaptive Horizon for Vehicle Trajectory Tracking Considering Crosswind Stability Zhiqun Yuan,Yanqiang Chen,Yuxuan Chang,Diansheng Huo,Li Lin 2024, 46 (10):  1829-1841.  doi: 10.19562/j.chinasae.qcgc.2024.10.010 Abstract ( 169 )   HTML ( 14 )   PDF (8342KB) ( 202 )   Save In order to extend the application scenario of model predictive control and improve the trajectory tracking accuracy of intelligent vehicles in extreme wind environment， an adaptive horizon control method considering crosswind stability is proposed. Firstly， taking the process of car overtaking on the sea-crossing bridge as the research object， the crosswind stability analysis model of car overtaking is established by using the coupling method of vehicle aerodynamics and system dynamics. Then， the safety risk model of vehicle lateral motion is established， and the adaptive horizon regulator is designed taking into consideration of lateral motion risk level， vehicle speed and lateral error， so as to realize the dynamic adjustment of prediction horizon and control horizon. Finally， CarSim and Simulink are used to build a joint simulation scenario， and the overtaking trajectory is planned by quintic polynomial to verify the tracking accuracy and robustness of the controller. The results show that compared with the fixed horizon and variable weight model predictive controller， the improved controller can better resist the aerodynamic interference of ' wind-vehicle-bridge' and improve the vehicle trajectory tracking accuracy at a lower real-time cost， with significant improvement in vehicle crosswind stability. Figures and Tables | References | Related Articles | Metrics

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Research on the Estimation Method of Road Friction Coefficient Ahead Based on Point Cloud Reflection Properties Hongyu Hu,Minghong Tang,Fei Gao,Mingxi Bao,Zhenhai Gao 2024, 46 (10):  1842-1852.  doi: 10.19562/j.chinasae.qcgc.2024.10.011 Abstract ( 121 )   HTML ( 5 )   PDF (6153KB) ( 205 )   Save The road friction coefficient is a significant factor that impacts the decision-making control strategy of the autonomous driving system. To achieve prospective and high-precision perception of the road friction coefficient， a novel estimation method for road friction coefficient based on the LiDAR equipped in vehicles is proposed in this paper. Firstly， a road dataset is constructed by collecting data from dry asphalt， concrete， wet asphalt， icy， and snowy road surface. Then， road point cloud is extracted using cloth simulation filtering and RANSAC algorithms， and abnormal noise points are removed based on Gaussian filtering. The road surface is divided into different regions according to the variation of point cloud reflectivity with distance and incident angle， and features are extracted accordingly. A road recognition model is constructed based on the deep neural network and trained by the collected dataset. Finally， the friction coefficient of the road ahead is determined based on the statistical experience of road material and peak friction coefficient. The test results show that the proposed algorithm achieves road type recognition accuracy of over 99.3%， with an average running cycle of 55ms， enabling real-time and high-precision estimation of the road peak friction coefficient. Figures and Tables | References | Related Articles | Metrics

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Driver-Automation Shared Lane-Keeping Robust Control Junhui Zhang,Xiaoman Guo,Yuxi Liu,Mingqiang Zheng,Yuhan Qian,Yuxuan Ding 2024, 46 (10):  1853-1862.  doi: 10.19562/j.chinasae.qcgc.2024.10.012 Abstract ( 144 )   HTML ( 6 )   PDF (5413KB) ( 272 )   Save In order to enhance the ability of the intelligent control system to predict the driver's steering intention during the co-driving， an indirect shared lane-keeping robust control algorithm is thus proposed in this paper. Firstly， a driver steering model that mimics the driver’s steering behavior is introduced， with the parameters identified offline by the immune genetic algorithm （IGA）. Then a linear time-varying human-vehicle-road model for derivers in the loop is established. Secondly， considering factors such as road curvature disturbance under complex working conditions， insufficient adaptation of the linear model， and time-varying characteristics of the model parameters， an output feedback \gamma suboptimal H∞ robust controller based on T-S fuzzy control theory is designed. Then， by fully taking into account both the driver’s steering behavior and the vehicular comprehensive lateral error， a human-vehicle control allocation strategy is designed to realize dynamic smooth allocation of driving control rights. Finally， the robust control algorithm is validated and studied based on the driver in the loop integrated platform. The results show that the robust control algorithm using the human-machine control allocation strategy has good disturbance suppression effect and can effectively enhance cooperation during the co-driving process， improving the friendliness of human-machine cooperation. Figures and Tables | References | Related Articles | Metrics

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Vehicle Trajectory Prediction Method Based on Graph Convolutional Interaction Network Mengxi Wang,Yingfeng Cai,Hai Wang,Zhongyu Rao,Long Chen,Yicheng Li 2024, 46 (10):  1863-1872.  doi: 10.19562/j.chinasae.qcgc.2024.10.013 Abstract ( 232 )   HTML ( 16 )   PDF (2737KB) ( 362 )   Save Accurate prediction of the future trajectory of surrounding vehicles is crucial to the decision-making and motion planning of autonomous vehicle. Existing research tends to use Recurrent Neural Networks （RNN） to model the time interaction of vehicles， but its interpretability of vehicle interaction modeling is poor， ignoring the actual lane structure， and there are deficiencies in capturing the interaction between vehicles and the environment. To address this problem， in this paper， a vehicle trajectory prediction model based on graph convolutional interactive networks that considers lane topology constraints is proposed. The vehicle interaction relationship extraction module adds edge weights when constructing the spatial relationship of vehicles to consider their neighboring interaction， making the interaction more interpretable. The driving scene representation module aims to improve the accuracy of vehicle trajectory prediction by extracting lane topology from high-precision maps. The trajectory prediction module integrates the output of the above two modules and outputs the predicted future trajectory. This integration allows for more precise modeling of the interaction between road structures and vehicle driving trajectories. The experimental results show that compared with mainstream methods， this model has achieved good performance on the Argoverse dataset， improving the accuracy and rationality of vehicle trajectory planning under complex road structures. Figures and Tables | References | Related Articles | Metrics

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Research on Shift Strategy of 2DCT for Pure Electric Vehicle Based on Driving Condition Identification Zhipeng Cao,Yong Chen,Bolin He,Sen Xiao,Bingzhao Gao,Xuebing Yin 2024, 46 (10):  1873-1885.  doi: 10.19562/j.chinasae.qcgc.2024.10.014 Abstract ( 122 )   HTML ( 3 )   PDF (9432KB) ( 68 )   Save In order to enhance the economic performance of pure electric vehicles （EVs） while maintaining better dynamic performance， a real-time shifting strategy based on driving cycle recognition is proposed for the self-developed two-speed dry dual clutch transmission （2DCT） for EVs. A radial basis neural network is adopted to predict the vehicle speed and the optimal shifting points are extracted by dynamic programming for seven types of driving cycle. Then， a driving cycle recognition model based on similarity comparison is constructed to recognize vehicle-driving conditions so as to achieve real-time shifting. The simulation based on MATLAB/Simulink and the 2DCT bench experiments are completed. The results demonstrate that the proposed real-time shifting strategy based on condition recognition can simultaneously meet the requirements of economic performance and shift frequency. Figures and Tables | References | Related Articles | Metrics

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Thermal Uniformity Analysis of Local Duplex Module for Power Battery Packs Hongyi Liang,Pu Huang,Wanli Liu,Jiqing Chen,Bingda Mo 2024, 46 (10):  1886-1896.  doi: 10.19562/j.chinasae.qcgc.2024.10.015 Abstract ( 107 )   HTML ( 10 )   PDF (5446KB) ( 208 )   Save The module arrangement of passenger car power battery packs usually has three different forms： single-layer arrangement， duplex arrangement and local duplex arrangement. The local duplex arrangement， which combines the characteristics of the other two methods， is more widely used. However， the study of electric vehicle fire accidents shows that battery packs with local duplex arrangement account for a higher proportion of spontaneous combustion accidents， which indicates that this arrangement may adversely affect the thermal uniformity of the battery module. In view of this， in this paper taking an electric vehicle battery pack with local duplex arrangement as the research object， a three-dimensional numerical model of the battery pack is established and the accuracy of the model is verified by comparing the experimental data with the simulation results. Using the validated model， the module temperature distribution characteristics of the battery pack under fast charging and three discharge rates are analyzed by numerical calculation methods， revealing the thermal uniformity of the local duplex arrangement of the modules under these conditions， especially the with the temperature difference of the double-layer module in the local duplex module larger than that of the single-layer module. In addition， the effect of coolant inlet temperature and flow rate on the thermal uniformity of the modules is explored respectively. It is found that attempting to reduce the coolant inlet temperature to improve the thermal uniformity of the module has limited effect， while increasing the coolant inlet flow rate can only reduce the temperature difference of the module under high discharge rate conditions， without obvious effect under low discharge rate conditions. This study provides a meaningful reference for the development and design of the battery thermal management system with local duplex modules. Figures and Tables | References | Related Articles | Metrics

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RUL Estimation Method for Lithium-ion Batteries Based on Multi-dimensional and Multi-scale Features Qiuyan Zhang,Ze Cheng,Xu Liu 2024, 46 (10):  1897-1903.  doi: 10.19562/j.chinasae.qcgc.2024.10.016 Abstract ( 139 )   HTML ( 5 )   PDF (2734KB) ( 404 )   Save Accurately predicting the remaining useful life （RUL） of lithium-ion batteries is important for the efficient and safe operation of energy storage systems. For the deficiencies of existing data-driven methods for RUL estimation， which extract aging features non-comprehensively enough and need to predict health state changes before estimating RUL， a RUL estimation method using multi-dimensional and multi-scale features is proposed in this paper to directly estimate the RUL of a battery using constant-current charging voltage segment data. The model maps RUL by extracting aging features of voltage segments using different scale convolution operation after dimensionally transforming the data. The model is validated using publicly available datasets from the University of Oxford， NASA， and the University of Maryland. The validation results show that the model can directly estimate the RUL of the batteries using the voltage segment data without the need of the current historical SOH of the batteries as the training data， which has higher accuracy and universality compared to fixed scale features based on a single dimension. Figures and Tables | References | Related Articles | Metrics

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Development and Validation of a Biomechanical Model with Anthropometry of 50th Percentile Chinese Male Occupant for Automobile Safety Haiyan Li,Pengfei Mu,Yanxin Wang,Linghua Ran,Shihai Cui,Lijuan He,Lü Wenle,Shijie Ruan 2024, 46 (10):  1904-1919.  doi: 10.19562/j.chinasae.qcgc.2024.10.017 Abstract ( 139 )   HTML ( 12 )   PDF (11164KB) ( 158 )   Save The biomechanical human body model with detailed anatomical characteristics is urgently needed computational tool for the intelligent and digital development in the field of automotive safety. In this study， based on the latest Chinese adult body size standards， a biomechanical model of the 50th percentile Chinese male anthropometry car passenger （TUST IBMs M50-O） is developed with independent intellectual property rights. By reconstructing 3 sets of frontal blunt impacts， 5 sets of side blunt impacts， and 3 sets of sled cadaver or volunteer tests， the C-NCAP deformable moving barrier side impact test is simulated to verify the effectiveness and application value of the developed model from multiple angles and all directions. The results show that all the 11 sets of reconstructed experimental data are within the corresponding cadaveric and volunteer corridors， with an average difference of approximately 10%， verifying the effectiveness of the model. The TUST IBMs M50-O model and WorldSID 50th model exhibit the same kinematic responses in the side impact simulation. However， due to the compression from the upper extremity and elbow joint of the TUST IBMs M50-O model， the peak resultant accelerations of T4 and T12 thoracic spine reach 43.5g and 47.3g， higher than the values of 38.5g and 41.2g of the WorldSID 50th model. The TUST IBMs M50-O model is also used to analyze the human tissue level injury risks based on stress and strain. In conclusion， the TUST IBMs M50-O model exhibits high biofedility and can be used to investigate the impact of injury mechanisms and virtual testing. As a reliable computational tool， this model can offer technological support to the research and development of safety protection devices in intelligent vehicles and intelligent high-end equipment. Figures and Tables | References | Related Articles | Metrics

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Effect of Driver Posture on Injury Risk Under AEB Conditions Yong Han,Yuecong Zhang,Mingwang Li,Di Pan,Haiyang Zhang 2024, 46 (10):  1920-1927.  doi: 10.19562/j.chinasae.qcgc.2024.10.018 Abstract ( 141 )   HTML ( 6 )   PDF (1995KB) ( 275 )   Save Driver posture in frontal crash conditions with and without autonomous emergency braking （AEB） has a significant impact on kinematic response and injury risk. In this paper， the THUMS （Ver.6.1） human finite element model is used to establish three driving postures， including standard， rearward recline， and forward recline， and a frontal collision constraint system model is established to conduct six sets of 50 km/h simulation tests for comparative analysis of the kinematic response of different driver postures with and without AEB， as well as the injury parameters of driver’s head and chest. The results show that the risk of head injury is highest in the recline posture with and without AEB intervention， with the HIC15 of 817.5 and 626.9 with and without AEB， respectively. The intervention of AEB has the greatest effect on the driver's chest compression， which is increased by 89%， 115%， and 22% for the three postures， respectively. The chest compression in the reward recline posture suffers the most serious injury. The results clarify the effect of driving posture and AEB on driver kinematic response and head and chest injuries， providing a reference value for the development and design of automotive restraint systems and AEB. Figures and Tables | References | Related Articles | Metrics

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Numerical Simulation and Experimental Study of Four Salient Poles Liquid-Cooled Electromagnetic Retarder Kai Zhang,Jiahan Bai,Liqiang Chen,Yangyang Lu,Weiyan Dong,Jigao Niu 2024, 46 (10):  1928-1936.  doi: 10.19562/j.chinasae.qcgc.2024.10.019 Abstract ( 94 )   HTML ( 5 )   PDF (63414KB) ( 81 )   Save To solve the problem of significant degradation in braking efficiency of medium and heavy-duty vehicles during prolonged downhill descents due to frequent engagement of the main brake system， a four salient poles liquid-cooled electromagnetic retarder structure is proposed in this paper. A vehicle downhill dynamics model is established to analyze the braking demand， using the magnetic equivalent circuit method to calculate its braking torque， and using the finite element method to numerically analyze its braking characteristics. The hierarchical variable domain fuzzy control strategy combined with a retarder is used for vehicle downhill braking control， and the controller and overall vehicle downhill braking model are established using MATLAB/Simulink for joint simulation. A 2 100 N·m prototype is designed. The braking characteristics of the prototype are tested through bench test and on-road vehicle tests. The results show that the actual measured value and the calculated value is basically consistent， with an average error within 5%， with the braking torque reaching 2 200 N·m when the speed is 1 250 r/min， which can meet the needs of medium and heavy duty vehicle braking. Figures and Tables | References | Related Articles | Metrics