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Table of Content

    25 July 2024, Volume 46 Issue 7 Previous Issue   
    Optimization of Net Power for PEMFC System Considering the Achievable Range of Operating Parameters
    Yinsong Xu,Wenhao Li,Changqing Du,Fuwu Yan
    2024, 46 (7):  1137-1146.  doi: 10.19562/j.chinasae.qcgc.2024.07.001
    Abstract ( 228 )   HTML ( 18 )   PDF (3881KB) ( 366 )   Save

    The operating parameters of the PEM fuel cell stack have an impact on the performance of the stack output as well as the parasitic power of auxiliary devices such as air compressor, recirculation pump and cooling fan. System maximum net power output goals can be achieved by optimizing the operating parameters of the fuel cell stack. Forthe actual system, constrained by the performance of the air compressor and the regulating capacity of the backpressure valve, the adjustment range of cathode operating parameters is limited. In this paper, the 62 kW fuel cell system model is established based on MATLAB/Simulink. Through simulation analysis, the achievable ranges of parameters optimization under various load currents are determined. Genetic algorithm is employed to optimize the fuel cell stack temperature, cathode pressure, and oxygen excess ratio. The results show that increasing the temperature of the fuel cell stack at various load currents is advantageous for enhancing the system's net power, with the optimal operating temperature being 80 ℃. However, the optimization direction for the oxygen excess ratio and cathode pressure varies at different load currents. At low load current (50, 100 A), increasing the oxygen excess ratio and cathode pressure results in a smaller growth in stack output power compared to the parasitic power. Providing lower oxygen excess ratio and cathode pressure is advantageous for enhancing the net power of the system. At high load current (300 A), low oxygen excess ratio and cathode pressure will limit the output power of the stack, with the lowest net power of only 35.530 kW. After the oxygen excess ratio and cathode pressure are reasonably increased, the optimal net power is 53.271 kW, and the net power can be increased by 49.9% through the optimization of operating parameters.

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    Design of a Multi-functional Test Platform for Fuel Cell Hydrogen Supply System
    Silong Zhang,Manzhi Liang,Hengkai Sun,Jicheng Chen,Hui Zhang
    2024, 46 (7):  1147-1156.  doi: 10.19562/j.chinasae.qcgc.2024.07.002
    Abstract ( 216 )   HTML ( 18 )   PDF (4969KB) ( 342 )   Save

    In recent years, with the continuous enhancement of fuel cell power, hydrogen supply systems have been evolving towards blind-end anode topologies with hydrogen circulation. However, research on testing systems for hydrogen supply and circulation lags noticeably, particularly in the performance testing of core components such as hydrogen circulation pumps and injectors. Therefore, a multifunctional testing platform for fuel cell hydrogen supply systems is developed in this paper, enabling component testing, characteristic data acquisition, offline calibration, and other functionalities for hydrogen circulation systems with different configurations. The platform, by simulating the pressure drop, hydrogen consumption, and the production of water and heat in real fuel cells, mitigates the additional cost incurred by testing on the performance and lifespan of actual fuel cells. Ultimately, based on this platform, an anode pressure control and anode purge control test are conducted on the hydrogen supply system of a 150 kW fuel cell, verifying the capability of the developed testing platform to meet specific testing requirements for different loads.

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    Study on Forming Performance of Aluminum-Plastic Film for Pouch Lithium-Ion Batteries
    Daolin Deng
    2024, 46 (7):  1157-1166.  doi: 10.19562/j.chinasae.qcgc.2024.07.003
    Abstract ( 250 )   HTML ( 15 )   PDF (3051KB) ( 328 )   Save

    To ensure the reliability of aluminum-plastic film encapsulation for lithium-ion batteries, strict control of the aluminum layer thickness after forming is necessary. However, obtaining the thickness relies heavily on physical experiments, resulting in high cost for both early design optimization and later production process quality monitoring. In this paper, a combination of physical experiments and simulation modeling is adopted to establish a constitutive equation that can well characterize the mechanical properties of the pouch during forming. Additionally, a prediction method for the aluminum layer thickness based on the overall aluminum-plastic film thickness is proposed, enabling precise prediction of the aluminum-plastic film and aluminum layer thickness after forming. Furthermore, based on simulation Design of Experiments (DOE), key influencing factors are screened to construct a response surface model, facilitating rapid prediction and optimal parameter matching design for different products, which also provides a solution for online monitoring of forming quality during production. The results show that the multi-layer composite aluminum-plastic film exhibits obvious anisotropy during the plastic stage. The 3-parameters Barlat-Lian constitutive model effectively represents the anisotropic properties of the film, and outperforms the single-directional elastic-plastic model, achieving accurate prediction of the aluminum-plastic film performance after forming. The constructed response surface model can replace the refined finite element model, and have excellent prediction accuracy for the thickness of the composite aluminum-plastic film and the aluminum layer, with an error less than 5%. By optimizing the process parameters, the formed thickness of the aluminum layer can be increased by 10%~20%. The integrated development application APP can meet the requirements for quick design evaluation, parameters optimization, and online monitoring of the forming quality.

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    Diagnosis for Battery Module Inconsistencies Based on Electrochemical Impedance Spectroscopy
    Hanxin Yao,Xueyuan Wang,Yongjun Yuan,Haifeng Dai,Xuezhe Wei
    2024, 46 (7):  1167-1176.  doi: 10.19562/j.chinasae.qcgc.2024.07.004
    Abstract ( 180 )   HTML ( 9 )   PDF (4250KB) ( 194 )   Save

    There may be inconsistencies in temperature, charge state, aging state (capacity and internal resistance) between individual cells in a battery module. Due to the existence of the "short board effect", the inconsistencies will affect the overall performance of the battery module, so timely and accurate inconsistencies diagnosis is very necessary. Considering that the above-mentioned inconsistencies will affect the electrode process characteristics, which will be reflected in the Electrochemical Impedance Spectroscopy (EIS) and Distribution of Relaxation Time (DRT), in this paper, after clarifying the effect of several kinds of inconsistencies on EIS and DRT by combining the equivalent circuits, an inconsistencies diagnosis method for battery modules based on EIS and DRT is innovatively proposed. The performance of unsupervised clustering algorithms such as K-means, AP (Affinity Propagation) and DBSCAN (Density Based Spatial Clustering of Applications with Noise) is comparatively analyzed by mixing the abnormal batteries into a group of batteries with good consistency. The results show that the DBSCAN diagnostic accuracy is 99.2%, which can realize the accurate diagnosis of the inconsistency difference of single cells within the battery module.

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    Fault Diagnosis Method for Power Battery Based on Quantification of Cell Abnormality with 1dCNN-LSTM
    Jiqing Chen,Yujia Feng,Fengchong Lan,Ping Wang
    2024, 46 (7):  1177-1188.  doi: 10.19562/j.chinasae.qcgc.2024.07.005
    Abstract ( 192 )   HTML ( 8 )   PDF (5286KB) ( 660 )   Save

    Accurate performance evaluation of power battery cells is of great significance to ensuring the safety of power batteries. For the existing data-driven battery fault diagnosis algorithms, mostly individual cells are compared with each other and the outlier cells are identified as faulty cells by classification, based on differences in characteristic parameters such as single cell voltage. However, if there are multiple cells of similar abnormally performance in the power battery pack, or all individual batteries show an overall performance deterioration, it is difficult to distinguish individual cells or even there is no significant outliers, and the application of the mutual comparison strategy is limited. A power battery fault diagnosis method is proposed based on 1dCNN-LSTM to quantify the abnormality of a single cell in this paper. Combining the three types of characteristics of vehicle motion status, drive system status and power battery electrical signal, the 1dCNN-LSTM fusion model is established to estimate the individual cell voltage under ideal conditions as reference. The difference between the real-time voltage reference value and the measured voltage value is used to quantify the abnormality of each cell. Combined with actual cases, it is shown that for thermal runaway case due to single cell failure, the abnormal performance of the faulty cell compared to others can be identified 7 days before accident, and potential risk can be recognized in discharge processes from a year of more before the accident. For overall deterioration cases without obvious individual cells inconsistency, the deterioration evolution within the last 7 days can be tracked.

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    Research on Early Fault Diagnosis of Lithium Battery Based on WOA-VMD and Shannon Entropy
    Jie Hu,Yayu Cheng,Hai Yu,Chaoming Jia,Haihua Qing
    2024, 46 (7):  1189-1196.  doi: 10.19562/j.chinasae.qcgc.2024.07.006
    Abstract ( 127 )   HTML ( 8 )   PDF (4084KB) ( 340 )   Save

    A lithium battery early fault diagnosis method based on WOA-VMD and Shannon entropy is proposed in this paper to solve the problem of current battery management systems being unable to diagnose early faults. Firstly, the whale optimization algorithm is introduced to optimize the parameters of the variational mode decomposition algorithm to improve its decomposition performance and obtain intrinsic mode function components containing more fault feature information. Then, the voltage signal of the individual battery is decomposed and reconstructed to reduce the impact of measurement noise and additional excitation voltage. Furthermore, a sliding window is used to calculate the Shannon entropy range of individual voltage and the overall Shannon entropy of individual voltage dispersion to set appropriate thresholds for early fault diagnosis. After verification with actual vehicle data, this method can provide fault warning about 10 minutes in advance without generating false warnings for vehicles without faults. It has strong robustness and reliability.

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    Research on Learning-Based Model Predictive Path Tracking Control for Autonomous Vehicles
    Mo Han,Hongwen He,Man Shi,Wei Liu,Jianfei Cao,Jingda Wu
    2024, 46 (7):  1197-1207.  doi: 10.19562/j.chinasae.qcgc.2024.07.007
    Abstract ( 357 )   HTML ( 22 )   PDF (3435KB) ( 642 )   Save

    For the trade-off between prediction model accuracy and computational cost for path tracking control of autonomous vehicles, a learning-based model predictive control (LB-MPC) path tracking control strategy is proposed in this paper. A two-degree-of-freedom single-track vehicle dynamic model is established, and an in-depth analysis is conducted on its step response error with respect to variation in vehicle speed, pedal position, and front wheel steering angle compared to the IPG TruckMaker model. Methods for constructing error datasets and receding horizon updates are designed, and the Gaussian process regression (GPR) is employed to establish an error-fitting model for real-time error compensation and correction of the nominal single-track model. The error correction model is utilized as the prediction model, and a path tracking cost function is designed to formulate a quadratic programming optimization problem, proposing a learning-based model predictive path tracking control architecture. Through joint simulation using the IPG TruckMaker & Simulink platform and real vehicle experiments, the real-time performance and effectiveness of the proposed GPR error correction model and LB-MPC path tracking control strategy are verified. The results show that compared to the traditional model predictive control (MPC) path tracking control strategy, the proposed LB-MPC strategy reduces the average path tracking error by 23.64%.

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    Multi-object Detection Algorithm Based on Point Cloud for Autonomous Driving Scenarios
    Le Tao,Hai Wang,Yingfeng Cai,Long Chen
    2024, 46 (7):  1208-1218.  doi: 10.19562/j.chinasae.qcgc.2024.07.008
    Abstract ( 250 )   HTML ( 10 )   PDF (5024KB) ( 530 )   Save

    The three-dimensional object detection algorithm based on point cloud is one of the key technologies in the autonomous driving system. Currently, the voxel-based anchor-free detection algorithm is a research hotspot in academia, but most researches focus on designing complex refinement stage, at the expense of huge algorithm latency, to bring limited performance improvement. Although the single-stage anchor-free point cloud detection algorithm has a more streamlined detection process, its detection performance cannot satisfy the needs of autonomous driving scenarios. In this regard, based on the anchor-free detection algorithm CenterPoint, a single-stage anchor-free point cloud object detection algorithm for autonomous driving scenarios is proposed in this paper. Specifically, the encoding and decoding sparse module is introduced in this paper, which greatly promotes the information interaction of the spatial non-connected areas of the three-dimensional feature extractor, ensuring that the three-dimensional feature extractor can extract features that satisfy various target detection. In addition, considering that it is challenging to adapt the existing two-dimensional feature fusion backbone to the center-based head, in this paper self-calibrated convolution and large kernel attention modules are introduced in to effectively extract point cloud features of the target area, which are then gathered into the center point area, thereby improving the algorithm's recall and accuracy of the target. The proposed algorithm in this article is trained and experimentally verified on the large-scale public dataset of nuScenes. Compared with the benchmark algorithm, mAP and NDS are increased by 5.97% and 3.62% respectively. At the same time, the actual road experiments with the proposed algorithm are conducted on a self-built vehicle platform, further proving the effectiveness of the proposed algorithm.

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    Trajectory Prediction Method Enhanced by Self-supervised Pretraining
    Linhui Li,Yifan Fu,Ting Wang,Xuecheng Wang,Jing Lian
    2024, 46 (7):  1219-1227.  doi: 10.19562/j.chinasae.qcgc.2024.07.009
    Abstract ( 201 )   HTML ( 6 )   PDF (3479KB) ( 217 )   Save

    To address limitation in prediction accuracy and data utilization efficiency of supervised learning-based trajectory prediction models, a trajectory prediction model and a general self-supervised pretraining strategy are proposed. Firstly, a lightweight trajectory prediction model based on Transformer is established to extract temporal-spatial features while modeling interaction relationship. Secondly, three types of masks, namely motion information temporal mask, road information spatial mask, and interaction relationship mask, are designed for self-supervised pre-training tasks on the model to enhance the model's ability to extract general scene features. Finally, pretraining weights are used as initialization parameters for supervised learning fine-tuning in downstream tasks. Experimental results on the Argoverse2 Motion Forecasting dataset show that the model can effectively reconstruct traffic scenes in pretraining tasks. The introduction of self-supervised pretraining improves prediction accuracy and data utilization efficiency. Moreover, it exhibits universality for different prediction tasks, achieving a 3.3% and 3.7% improvement in the minFDE6 for single-agent and multi-agent trajectory prediction tasks, respectively.

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    Multi-sensor Data Fusion for Intelligent Vehicles Based on Tripartite Graph Matching
    Luxing Li,Chao Wei
    2024, 46 (7):  1228-1238.  doi: 10.19562/j.chinasae.qcgc.2024.07.010
    Abstract ( 136 )   HTML ( 9 )   PDF (4336KB) ( 342 )   Save

    Multi-sensor fusion is an effective way to improve intelligent vehicle perception. For the data-matching problem of the three types of sensors of LiDAR, millimeter-wave radar, and camera, traditional methods such as bipartite graph matching can’t achieve high precision, with poor matching robustness. Therefore, a multi-sensor data fusion algorithm for intelligent vehicles based on tripartite graph matching is proposed in this paper. The problem of data matching of the three sensors is abstracted as a weighted tripartite graph-matching problem. By using Lagrange relaxation, the original problem space is decomposed into subspaces, the weights of vertices and edge inside which are determined then by the cost matrix model. Furthermore, combining the perceptual error model and likelihood estimation, the posterior distribution of perceptual errors is determined. Ultimately the Lagrange Multiplier (LM) model is used for data matching. Finally, the effectiveness of the proposed matching algorithm is validated by the nuScenes training dataset and real-world vehicle tests. On the dataset, the proposed algorithm improves F1 scores by 7.2% compared to common algorithms. In various real-world vehicle scenarios, the proposed algorithm shows excellent perceptual accuracy and robustness across.

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    A Multi-modal Data Mining Algorithm for Corner Case of Automatic Driving Road Scene
    Hai Wang,Guirong Zhang,Tong Luo,Meng Qiu,Yingfeng Cai,Long Chen
    2024, 46 (7):  1239-1248.  doi: 10.19562/j.chinasae.qcgc.2024.07.011
    Abstract ( 273 )   HTML ( 23 )   PDF (5240KB) ( 850 )   Save

    The development of visual perception technology based on deep learning is beneficial for the advancement of environment perception technology in automatic driving systems. However, for corner cases of autonomous driving scenario, there are still some problems in the current perception model. This is because the ability of the perception model based on deep learning depends on the distribution of the training dataset. Especially when categories in the driving scene never appear in the training set, the perception system is often fragile. Therefore, identifying unknown categories and extreme scenarios remains a challenge for the safety of automatic driving perception technology. From the perspective of processing data sets, in this paper a novel multimodal automatic corner case mining process called "Corner Case Mining Pipeline (CCMP)" is proposed. In order to verify the effectiveness of "CCMP", the concern case subset "Waymo-Anomaly" on the basis of Waymo open datasets is established, with a total of 3 200 images, each of which will contain the corner case scene defined in the text. Then based on the private data set Waymo-Anomaly, it is proved that the recall rate of "CCMP" corner case mining can reach 91.7%. In addition, the effectiveness of object detectors targeting long-tailed distributions in datasets containing corner case is experimentally verified. Ultimately, the authenticity of the automatic driving perception model in the real world is expected to improve from the perspective of datasets processing.

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    A Mapping and Planning Method Based on Simplified Visibility Graph
    Xiaolin Fan,Xudong Zhang,Yuan Zou,Xin Yin,Yingqun Liu
    2024, 46 (7):  1249-1258.  doi: 10.19562/j.chinasae.qcgc.2024.07.012
    Abstract ( 159 )   HTML ( 11 )   PDF (3466KB) ( 263 )   Save

    Most of the current vehicle route planning is based on the grid map planning method, which will greatly increase the amount of calculation when the search area is large. In contrast, the method based on visibility graph can reduce the amount of calculation during path search, but is greatly affected by the complexity of obstacles. For this problem, combining the SLAM and visibility graph methods, a simplified visibility graph construction and planning method is proposed in this paper. Firstly, the improved SLAM algorithm is used to generate point cloud maps, and dynamic obstacles are removed. Then a visibility graph is generated, and the complex edges of polygons in the visibility graph are simplified based on the size of the obstacle and the size of the concave angle at the vertex to eliminate redundant vertices. Finally, through simulation experiments and real vehicle experiments, it is proved that compared with the original algorithm, this method can reduce the number of polygon vertices in the visibility graph by 20%-30% while ensuring the accuracy of mapping. The map update time and the running time of the overall algorithm are also reduced by more than 30%. It shows that the method in this paper can effectively reduce the amount of calculation and the running time of the algorithm in the mapping and planning process.

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    Real-Time Online Prediction of Transient Temperature Field for Electric Drive Assembly with Multi-physics Coupling and Data-Driven Fusion Modeling
    Peng Tang,Zhiguo Zhao,Haodi Li,Wancheng Lu,Jianyu Yang
    2024, 46 (7):  1259-1272.  doi: 10.19562/j.chinasae.qcgc.2024.07.013
    Abstract ( 209 )   HTML ( 6 )   PDF (10924KB) ( 258 )   Save

    It is crucial to develop a lightweight real-time online temperature prediction model for electric drive assembly (EDA) to effectively monitor its future abnormal temperature state in advance and ensure vehicle safety. Based on multi-physics coupling and data-driven fusion modeling, this paper proposes an online prediction method for the transient temperature field of EDA. Firstly, a multi-physical coupling finite element model of EDA electric-magnetic-thermal-flow multi-physics coupling is established, and the accuracy of the model is verified by bench test. Secondly, several transient temperature field datasets under normal working conditions are generated via multi-physical field coupling model for subsequent proxy model verification. Then, combined with the finite element model to obtain the simplified thermal network topology and the graph convolutional neural network, a relational spatial-temporal graph convolutional neural network prediction model driven by model and data is proposed. Finally, the effectiveness and real-time performance of the proposed temperature prediction model are verified by offline simulation and online test under different ambient temperatures and working conditions. Analysis results on the measured offline dataset show that the global prediction error and average absolute error are 4.4 and 1.25 ℃, reduced by 17.3%, 28.1%, 5.3% and 29.3%, respectively, compared with the conventional temporal graph convolutional neural network and gated recurrent unit. Meanwhile, the online prediction results of the bench are also very close to the real measured values, with the global prediction error and average absolute error of 3.99 and 0.66 ℃. In conclusion, the proposed real-time on-line temperature prediction method can accurately predict the real temperature change of EDA.

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    Intelligent Design and Analysis of Body Structure Based on Data Drive
    Rong Cao,Junwei Hua,Yongcheng Li,Fangli Guo,Wenbin Hou
    2024, 46 (7):  1273-1281.  doi: 10.19562/j.chinasae.qcgc.2024.07.014
    Abstract ( 197 )   HTML ( 9 )   PDF (2390KB) ( 466 )   Save

    As an important stage of the automotive design process, conceptual design requires rapid conceptual design and evaluation. The current methods generally use a combination of parametric design and CAE to achieve analysis based conceptual design of car body structures. With the development and maturity of machine learning and deep learning algorithms, intelligent design methods will become the main innovative technology for body structure design. In this article, a combination of data-driven and optimization design method is used to independently develop the vehicle structure intelligent design software tool (S-iVCD). Firstly, based on residual networks and thermal map regression algorithms, feature points of the vehicle body structure are extracted to achieve automated modeling of the conceptual model of the vehicle body structure. Secondly, based on Gaussian process sampling, a body structure dataset is collected and a fully connected neural network model is used to construct the body structure network model. The parameters of various components of the vehicle body can be input into the trained network model to obtain the overall performance results of the vehicle body. Finally, by combining data-driven computing with the moving asymptote algorithm, a multi-objective optimization design of the vehicle body structure that considers mass, bending stiffness, and torsional stiffness is quickly achieved. By comparing with finite element examples, the error of the calculation results is within the allowable range, with the optimization calculation time greatly shortened, and the lightweight rate reaching 7.4%. This indicates that the data-driven body structure optimization design method is effective in improving efficiency in the conceptual design stage of automobiles.

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    Research on Air Spring Modeling Based on Fractional Order and Electric Vehicle Active Suspension Control
    Guizhen Feng,Dongpeng Zhao,Shaohua Li
    2024, 46 (7):  1282-1293.  doi: 10.19562/j.chinasae.qcgc.2024.07.015
    Abstract ( 185 )   HTML ( 6 )   PDF (4675KB) ( 496 )   Save

    Electrically controlled air suspension (ECAS) has the function of adjusting suspension stiffness and body height, which can effectively improve vehicle ride comfort and handling stability. Taking a passenger car ECAS as an example, the viscoelastic damping characteristics of rubber airbag are described by fractional theory, and the thermodynamic model is optimized considering the equivalent damping and hysteretic characteristics, which is in good agreement with the experimental data, and the precision of the optimized air spring model is verified. On this basis, considering the longitudinal and lateral dynamic characteristics of the vehicle and the Dugoff tire model, a 14-degree-of-freedom vehicle ECAS dynamic model is established, and a Model Predictive Control (MPC) active suspension control method is proposed, with measurable variables as the input of the controller, to realize the active control under straight and turning driving conditions. Simulation and vehicle bench test show that the fractional correction model can well reflect the variable stiffness characteristics of ECAS, and the active suspension control strategy based on MPC can adjust the air spring stiffness in real time, control the body posture, and effectively improve the ride comfort and stability of the electric vehicle. The research method in this paper provides a new idea for vehicle suspension system modeling and active control.

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    Thermal Hysteresis Equivalent Mechanical Model of Compressed Air Inside Air Springs
    Junjie Chen,Jinyuan Xu,Yujie Shen,Lü Hui
    2024, 46 (7):  1294-1301.  doi: 10.19562/j.chinasae.qcgc.2024.07.016
    Abstract ( 105 )   HTML ( 1 )   PDF (2946KB) ( 287 )   Save

    The heat exchange effect of internal compressed air leads to strong thermal hysteresis and frequency correlation of air springs’ mechanical properties. Therefore, a thermal hysteresis equivalent mechanical model is constructed to describe the energy exchange process of compressed air inside air springs in this paper. Based on the rubber airbag modal, an air spring hysteresis mechanical characteristic model covering both rubber airbag hysteresis and compressed air thermal hysteresis is constructed, and an identification method for the key parameters of the model is provided. The experiments show that the maximum errors of the hysteresis loop and dynamic stiffness are less than 3.3% and 6.7%, respectively, verifying the accuracy of the hysteresis mechanical characteristic model. Finally, the inherent law of the thermal hysteresis of compressed air with frequency varying is revealed. The research results provide theoretical support for identifying the hysteresis nonlinear mechanism of air springs and its effective utilization.

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    A High Time-Resolution Reconstruction on the Automotive Turbulent Wake Based on LSTM-POD
    Zhigang Yang,Yujing Li,Chao Xia,Mengjia Wang,Lei Yu
    2024, 46 (7):  1302-1313.  doi: 10.19562/j.chinasae.qcgc.2024.07.017
    Abstract ( 94 )   HTML ( 12 )   PDF (6235KB) ( 557 )   Save

    A deep-learning LSTM-based POD model (LSTM-POD) based on long short-term memory (LSTM) and proper orthogonal decomposition (POD) is developed for the turbulent wake of the square-back Ahmed automotive general model. A high time-resolution reconstruction is achieved by establishing the mapping relationship between the POD modal coefficients of the non-time-resolved planar velocity field and the time-resolved velocity signals at a number of discrete points, and the effect of different time-step configurations, i.e., the single time step (LSTM-Sin) and multiple time steps (LSTM-Mul) on the reconstruction results is compared. The results show that the LSTM-POD model has strong learning and generalization ability in time series reconstruction, In addition, LSTM-Mul considers temporal continuity and correlation, the reconstructed mode coefficients (lower order) and velocity fields of which are more consistent with the POD reconstructed results compared with that of LSTM-Sin. The deep learning model proposed in this study can alleviate the problems of high resource consumption and low computational efficiency in obtaining high time resolution flow field data through experiments and high-precision numerical simulation.

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    Lightweight Design and Optimization of Integrated Die Casting Aluminum Alloy Front Cabin
    Zhiling Fang,Yanli Song,Jie Kang,Xinghong Zhang,Dan Zhang
    2024, 46 (7):  1314-1322.  doi: 10.19562/j.chinasae.qcgc.2024.07.018
    Abstract ( 212 )   HTML ( 15 )   PDF (3922KB) ( 676 )   Save

    The requirement of low carbon and lightweight in the auto industry is growing now. The new mega-casting technology applied on vehicle body can better achieve weight, cost and emission reduction, and has become spotlight to automobile manufacturers. In this paper, the traditional steel front compartment of passenger car body is replaced by integrated die casting part, and lightweight design on the aluminum alloy integrated front engine compartment is conducted. The optimal load path for stiffness is obtained through topology optimization of the front cabin by SIMP method. Considering the castability of the front cabin, the draft direction, thickness size and position distribution of the ribs are designed. Frontal impact simulation is conducted according to C-NCAP2021 and the impact resistance of the integrated die cast body is improved through Taguchi experimental design method and response surface optimization. Simulation analysis is conducted on the optimized performance of the white body. Compared with the traditional scheme, the weight of the optimal design is reduced by 13.9%, with the bending stiffness of the BIW increased by 9.7%, and the first modal meets the requirements. The research in this paper is meaningful for the platform design and industrialized application of integrated die casting car body structure in the future.

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    Crushing Analysis and Interval Optimization Design for Multi Cell Aluminum Alloy Thin-Walled Tubes
    Lijun Qian,Luxin Yu,Xianguang Gu,Wenyu Liang
    2024, 46 (7):  1323-1334.  doi: 10.19562/j.chinasae.qcgc.2024.07.019
    Abstract ( 96 )   HTML ( 2 )   PDF (5496KB) ( 415 )   Save

    Aluminum alloy multi-cell thin-walled tubes have better mechanical properties in energy absorption than ordinary square tubes in axial compression conditions, with a wide range of application prospects in automotive, aviation, military equipment, and other industries. To study the anisotropic characteristics of extruded 6061-T6 aluminum alloy material, uniaxial tensile mechanical properties tests are conducted on the sheet along the extrusion direction of 0°, 45°, and 90°. The corresponding stress-strain curves and anisotropic characteristic parameters are obtained, and the material constitutive model is established based on the yield criterion of anisotropic hardening behavior. Tubes with different cross-sectional configurations shaped as the Chinese characters of mouth, day and eye are designed and quasi-static crushing tests are conducted. By analyzing the deformation crushing force curve, it is shown that the thin-walled structure of the triple-cell alloy has superior crash resistance performance. In order to further obtain the optimal design parameters of the triple-shaped tube, considering the uncertain effect of material parameter fluctuations such as Poisson's ratio and elastic modulus on the structural impact resistance, the multi-cell aluminum alloy thin-walled tube impact resistance interval uncertainty optimization model is established. The interval possibility degree method is used to transform it into a deterministic problem. By combining the Artificial Neural Networks (ANNs) model with the Intergeneration Projection Genetic Algorithm (IP-GA) method, a double-layer nested optimization is performed on this problem to analyze the impact of different likelihood levels on uncertainty optimization results, providing guidance for the selection of different reliability optimization design.

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