Table of Content

25 May 2022, Volume 44 Issue 5 Previous Issue    Next Issue

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Research on Control Strategy of Predictive Adaptive Cruise Control of Heavy Duty Commercial Vehicle Xingkun Li,Xuguang Zheng,Guohui Wang,Yuhai Wang 2022, 44 (5):  649-655.  doi: 10.19562/j.chinasae.qcgc.2022.05.001 Abstract ( 396 )   HTML ( 35 )   PDF (1994KB) ( 542 )   Save In order to improve the economy and safety of commercial vehicle cruise system， a predictive adaptive cruise control system （PACC） is designed considering fuel saving and safe driving， and the economic speed of the predictive cruise is planned based on the road slope ahead. An optimized predictive adaptive cruise control strategy based on the front vehicle information is proposed to solve the problem of braking disturbance caused by the vehicle in front during cruise. Based on the gradient of the road ahead， the adaptive inter-vehicle distance is designed and the driving speed of the main vehicle is planned to realize the predictive adaptive cruising. A real vehicle test is carried out based on FAW JH6 heavy-duty commercial vehicle. The research results show that the algorithm can effectively reduce fuel consumption and driving fatigue， which provides extremely important theoretical and practical value for the development of commercial vehicle driving assistance system. Figures and Tables | References | Related Articles | Metrics

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Research on Cornering Trajectory Planning for Intelligent Vehicle Considering Trajectory Smoothness and Stability for Collision Avoidance Jiaxing Yu,Arab Aliasghar,Xiaofei Pei,Xuexun Guo 2022, 44 (5):  656-663.  doi: 10.19562/j.chinasae.qcgc.2022.05.002 Abstract ( 338 )   HTML ( 24 )   PDF (4263KB) ( 429 )   Save A trajectory planning is proposed for collision avoidance in a corner of intelligent vehicle considering trajectory smoothness and vehicle stability. The trajectory planning is decomposed into path planning and speed planning. The improved rapidly exploring random tree （RRT） is used to construct a collision-free continuous-curvature clothoids with minimum curvature change. Based on the measurement function of deep neural network， the improved RRT selects and connects the tree nodes with the smallest cost function， and searches the nearby nodes to find whether there are nodes with smaller cost function near the selected nodes. In speed planning， trapezoidal velocity profiles are used to output continuous target acceleration curve according to the road speed limit rules. Then， based on the curvature of clothoids and vehicle state， the target acceleration is dynamically adjusted by Preview G-Vectoring control （PGVC）. Finally， the final expected acceleration is obtained through acceleration control logic. The simulation results show that the proposed trajectory planning method can not only achieve collision avoidance in a corner and guarantee a good tracking performance， but also improve the stability in a corner at high speed. Besides， the paper also verifies the fast convergence， path smoothness and real-time performance based on parallel computing of RRT. Figures and Tables | References | Related Articles | Metrics

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Research on Model Predictive Control of Autonomous Vehicle Path Tracking Under Complex Road Condition Jun Li,Wenxing Wan,Sanqiang Hao,Wu Qin,Feifei Liu 2022, 44 (5):  664-674.  doi: 10.19562/j.chinasae.qcgc.2022.05.003 Abstract ( 405 )   HTML ( 27 )   PDF (4957KB) ( 596 )   Save To improve the path tracking accuracy， driving stability and safety of autonomous vehicle under complex road conditions of right angle turn， continuous curve and arc curve， an improved model predictive control （MPC） algorithm is proposed. The salient feature of the improved MPC is that the maximum longitudinal speed of vehicle without sliding on the flat road is determined by the curvature of the travel path， that is， the longitudinal speed of the vehicle is not assumed to be constant. Based on model predictive control， the vehicle kinematics model is established. Speed and front wheel angle are set as constraints. And position deviation and control increment are designed as the objective function to obtain the optimal front wheel angle and driving speed. Finally， with the experimental platform and test site provided by a new energy vehicle company， the path tracking effect of the improved MPC under complex road conditions and the model predictive control algorithm with constant longitudinal speed is compared and analyzed. The experimental results verify the effectiveness and superiority of the improved MPC， and indicate that the accuracy of the path tracking， the driving stability and the safety of vehicle are guaranteed. Figures and Tables | References | Related Articles | Metrics

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Pedestrian Trajectory Prediction and Risk Grade Assessment Based on Vehicle-Perspective Pedestrian Data Zheyu Zhang,Lü Chao,Jinghang Li,Guangming Xiong,Shaobin Wu,Jianwei Gong 2022, 44 (5):  675-683.  doi: 10.19562/j.chinasae.qcgc.2022.05.004 Abstract ( 200 )   HTML ( 11 )   PDF (2970KB) ( 245 )   Save The commonly used pedestrian trajectory and risk prediction model based on roadbed-perspective data often cannot avoid complex modeling calculation and manual judgment. For succinctly and effectively predicting pedestrian trajectory and evaluating risk grade， a pedestrian trajectory and risk grade prediction model is created based on vehicle-perspective pedestrian data in this paper. The acquisition of vehicle-perspective pedestrian data， the prediction of pedestrian trajectory based on long-short term memory neural network and the assessment of risk grade based on clustering analysis - support vector machine method are successively conducted. The results of experiments show that the data-driven model built based on vehicle-perspective pedestrian data can capture the movement tendency and interaction characteristics of pedestrian and vehicle and is capable of predicting pedestrian trajectory and assessing risk grade. Figures and Tables | References | Related Articles | Metrics

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Research on Style Transfer Network for Autonomous Driving Data Generation Dafang Wang,Jingdong Du,Jiang Cao,Mei Zhang,Gang Zhao 2022, 44 (5):  684-690.  doi: 10.19562/j.chinasae.qcgc.2022.05.005 Abstract ( 235 )   HTML ( 14 )   PDF (2897KB) ( 257 )   Save The data abundance of the autonomous driving dataset is the key to ensuring the robustness and reliability of autonomous driving algorithm based on deep learning， but the amount of data with night scenes and various climates and weather conditions in current autonomous driving datasets are still very limited. In order to meet the application needs in the field of unmanned driving， a style transfer network is built， which can convert the current autonomous driving data into various forms such as night and snow， etc. The network adopts a structure of single encoder-dual decoder， combined with various means such as semantic segmentation networks， skip connections， and multi-scale discriminators to improve the quality of generated images with good vision effects. Deeplabv3+ semantic segmentation network trained by real data is used to evaluate the images generated and the results show that the mean intersection over union of the images generated by the network adopted is 2.50 and 4.41 percentage points higher than that generated by AugGAN and UNIT networks with double encoder-double decoder structure respectively. Figures and Tables | References | Related Articles | Metrics

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K-means Complementary Iterative Vehicle Information Data Clustering Based on DHSSA Optimization He Huang,Wenlong Li,Lan Yang,Huifeng Wang,Biao Wang,Feng Ru 2022, 44 (5):  691-700.  doi: 10.19562/j.chinasae.qcgc.2022.05.006 Abstract ( 123 )   HTML ( 3 )   PDF (2493KB) ( 164 )   Save For the problems that the traditional method is greatly affected by the initialization center in the process of vehicle information data clustering， resulting in low clustering accuracy and poor robustness， and the selection of clustering center by calculating the mean in the iterative process is greatly affected by the outliers， a K-means complementary iterative vehicle information data clustering optimized by DHSSA is proposed. Firstly， for the problem of insufficient update of discoverer position and insufficient population diversity in SSA algorithm， a disturbance factor-head optimization strategy is designed. The influence of the optimal individual is strengthened by the adaptive head strategy， and the search space is expanded by the disturbance factor， which improves the accuracy of cluster center searching. Secondly， the initialization of cluster centers optimized by screening maximum and minimum distance product method （SMMP） is designed， and the screening mechanism is added on the basis of MMP， so that the initial centers are more evenly distributed in each cluster as much as possible. Finally， DHSSA and SMMP are integrated to optimize the K-means complementary iteration， which reduces the number of iterations and increases the search efficiency to obtain better clustering results. Using a variety of data sets for testing， through the convergence curve and performance indicators in the experimental results， it can be seen that the proposed DHSSA-KMC method is of higher search accuracy， convergence speed and lower clustering cost than SSA-KMC， IMFO-KMC， KMC and KMC++， and the time consumption is reduced compared with SSA-KMC and IMFO-KMC， which proves the effectiveness and superiority of the algorithm. In the process of vehicle information data processing， DHSSA-KMC can efficiently cluster and generate competitive models for consumers to choose， with obvious application value. Figures and Tables | References | Related Articles | Metrics

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EVCT on Combustion and Particulate Emission of Direct Injection Gasoline Engine Fangxi Xie,Xianglong Meng,Yu Liu,Xiaona Li,Yulin Zhang,Haizhou Feng 2022, 44 (5):  701-708.  doi: 10.19562/j.chinasae.qcgc.2022.05.007 Abstract ( 162 )   HTML ( 7 )   PDF (2890KB) ( 156 )   Save This paper studies the effect of exhaust valve closing timing （EVCT） coupled with ignition timing （IT） on combustion， performance and particulate emissions of spark-ignited gasoline direct injection （GDI） engines under five different operating conditions. The results of the study show that the delay of EVCT leads to prolonged combustion duration （CA5-CA90） and delayed combustion center （CA50）. The advancement of IT can advance CA50 and shorten the combustion duration. EVCT combined with IT can effectively reduce engine fuel consumption and particulate emission， with a reduction of brake specific fuel consumption（BSFC） by 4.66% and the total number of particulates by up to 95%. With the increase of the load or speed， the impact of EVCT on particulate emissions and fuel consumption weakens. Figures and Tables | References | Related Articles | Metrics

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Optimization Strategy of Thermal Management of Power Battery Pack Based on Iterative Dynamic Programming Yan Ma,Jiayi Li,Qian Ma,Mingchao Chen 2022, 44 (5):  709-721.  doi: 10.19562/j.chinasae.qcgc.2022.05.008 Abstract ( 264 )   HTML ( 10 )   PDF (4669KB) ( 297 )   Save During the driving of electric vehicles， the power battery continuously generates heat. Continuous high temperature will affect the battery life and the safety of the operation of electric vehicles. Therefore， it is necessary to adopt an efficient and energy-saving cooling optimization strategy in order to improve the efficiency of the power battery. Based on the heat generation characteristics of battery and Newton's law of cooling， a concentrated mass heat model of the battery pack is established in this paper， and compared with the battery liquid cooling model set up in AMESim to verify the accuracy of the model. In view of the high nonlinearity and time varying of the battery thermal management system， an iterative dynamic programming （IDP） strategy of iterative approximation of optimal control in multidimensional search space is proposed in this paper. Through the co-simulation of Matlab-AMESim， it shows that the proposed IDP optimization strategy can rapidly cool the temperature of the battery pack with minimum energy consumption， and the coolant flow rate is stable， which verifies the high efficiency and energy saving of the optimization strategy. Figures and Tables | References | Related Articles | Metrics

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Judgment of Critical Condition for Crash Safety of Lithium Battery Pack Based on Simplified Beam Element Model Guang Chen,Chenyang Wei,Xiaoyu Li,Guoxi Jing 2022, 44 (5):  722-729.  doi: 10.19562/j.chinasae.qcgc.2022.05.009 Abstract ( 152 )   HTML ( 9 )   PDF (4120KB) ( 176 )   Save In view of the difficulty in applying the existing battery cell finite element model to vehicle crash analysis due to its large number of elements and low speed of calculation， a beam-element-based finite element model， reflecting the crushing and bending characteristics of cell housing is established. The effectiveness of the simplified model for battery cell is verified by comparing the results of axial crushing， radial squishing and indenting tests of battery cell. Simulations are conducted on the proposed simplified model of battery pack with 6 x 4 cells under two conditions： battery pack impacting rigid wall and rigid wall （steel plate in fact） impacting battery pack to determine the critical impact speed and critical mass of impactor leading to deformation and short-circuit failure of battery. The results show that in the condition of battery pack impacting rigid wall， the second and third rows of battery pack near rigid wall begin to fail when the impact speed reaches 245 km/h； while in the condition of rigid wall impacting battery pack， the cell starting to fail when the mass of impator larger than 16.06 kg is in the second row of battery pack near impactor. Therefore， the position of the battery cell first failing should be determined according to different impact conditions. Figures and Tables | References | Related Articles | Metrics

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Study on the Performance and Safety of Aluminum Matrix Composite for Hydrogen Production Yang Zhao,Kang Ye,Hanqiao Sun,Zunyan Hu,Liangfei Xu,Jianqiu Li,Minggao Ouyang 2022, 44 (5):  730-735.  doi: 10.19562/j.chinasae.qcgc.2022.05.010 Abstract ( 171 )   HTML ( 9 )   PDF (3167KB) ( 254 )   Save The technology of aluminum water electrolysis for hydrogen production has the advantages of high hydrogen storage density， safe and environmentally friendly， becoming one of the hydrogen production technologies with high compatibility and being able to meet the needs of hydrogen supply of fuel cell in special scenes. It is described in this paper that the aluminum matrix composite of 85%Al-9%LiAlH4-3%Bi-3%NaCl is produced with high-energy ball milling process， the hydrogen production performance in different scenes and its safety under special environment are studied， and the stable supply of hydrogen flow is achieved through the design and production of the device of aluminum-water reaction for producing hydrogen. The results show that at a temperature of 50oC， the maximum hydrogen output of aluminum matrix composite， which has a good fire resistance property shown by burning experiment， reaches 1，435 mL/g， and the device designed for aluminum-water reaction can fulfill a stable hydrogen supply with a flow rate of 0.8 L/min， meeting the operation demand of low-power fuel cell. Figures and Tables | References | Related Articles | Metrics

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Research Progress of EMB Clamping Force Control and Sensor Fault Diagnosis Qixiang Zhang,Liqiang Jin,Bohao Jin,Yihan Zhang,Pengfei Chen,Yongteng Liu,Jianhua Li 2022, 44 (5):  736-746.  doi: 10.19562/j.chinasae.qcgc.2022.05.011 Abstract ( 636 )   HTML ( 20 )   PDF (2824KB) ( 586 )   Save The electro-mechanical brake system （EMB） uses electronic control pure mechanical brake technology to realize various active safety control functions. It has the advantages of simple structure， rapid response， independent and precise control of wheel braking torque. To comprehensively sort out the development status of the EMB system and clarify its future technology trend， the structure of the EMB is introduced first， and the advantages and disadvantages of several typical structure types of the EMB are analyzed and main research directions in the future are determined. Then， the research progress at home and abroad is reviewed from two levels of clamping force control and sensor fault diagnosis. The development history and the focus of future research of clamping force control algorithms are analyzed， and the experiments’ results of three typical clamping force control algorithms are compared.Then， the specific types and functions of sensor fault diagnosis are introduced， and the actual control effect of different fault diagnosis algorithms is analyzed through quantitative indicators. Finally， the problems faced by the EMB system and future development trends are analyzed and prospected. It is pointed out that further research can be concentrated on improving the accuracy and robustness of algorithms of clamping force control and sensor fault diagnosis， etc.， the coordinated controlling of the EMB and wire-controlled chassis integrated control technology and the influence of the EMB on the stability and comfort of the vehicle. Figures and Tables | References | Related Articles | Metrics

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Study on the Backstepping Control Algorithm for the Hydraulic Pressure in Electro-hydraulic Brake-by-wire System Qin Shi,Xin Liu,Helie Ying,Mingwei Wang,Zejia He,Lin He 2022, 44 (5):  747-755.  doi: 10.19562/j.chinasae.qcgc.2022.05.012 Abstract ( 170 )   HTML ( 3 )   PDF (2688KB) ( 195 )   Save In order to realize the accurate control of hydraulic pressure in brake-by-wire system， a new type of brake-by-wire system is developed in this paper. A control-oriented dynamics model of system is established through system dynamics analysis， and a backstepping control algorithm is designed based on that system model. A radial basis function network is used to approximate the characteristics of continuous function and estimate the non-linear friction， related to system state variables， as the compensation for backstepping controller， with the Lyapunov stability of the algorithm proved. An electro-hydraulic brake-by-wire test bench is built to conduct tests on several braking conditions and the results show that the control strategy designed can achieve the accurate control of hydraulic pressure in brake-by-wire system with fast response. Figures and Tables | References | Related Articles | Metrics

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Complicated Road Surface Identification Algorithm with Prevention of ASR False Activation Xichen Li,Hong Zhang,Xiaotian Xie,Bowen Wang,Xinyu Wang 2022, 44 (5):  756-763.  doi: 10.19562/j.chinasae.qcgc.2022.05.013 Abstract ( 169 )   HTML ( 3 )   PDF (1382KB) ( 179 )   Save The significant fluctuation of wheel speed affected by the dynamic load of uneven road surface （URS） can lead to jumps in wheel slip rate，which will frequently trigger the Acceleration Slip Regulation （ASR） with the slip rate as the control target. Therefore， it is necessary to identify uneven road surface and subsequently optimize the control strategy. For uneven road with bumps， an identification algorithm employing threshold logic method with the variation of slip rate and lateral camber angle as the judgement index. For continuous uneven road， an identification method integrates traversal counting method and energy method is adopted to determine the road condition and take the area enclosed by slip rate and road surface adhesion coefficient as the characteristic value to judge the adhesion condition of the road surface. Furthermore， ASR threshold adjustment control is used to mitigate the slip phenomenon caused by wheel suspension and the non-ideal yaw resulted from the active braking imbalance. The results indicate that the algorithm can identify the condition of URS rapidly and precisely with an 18.8% reduction of the active braking duration， minimizing the power loss when driving on URS. Figures and Tables | References | Related Articles | Metrics

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Research on the Effects of Emergent Avoidance Postures of Electric Two-wheeler Riders on Their Injury Risk Yong Han,Liya Lin,Yong He,Di Pan,Hongyu Cai,Qian Peng,Hao Feng 2022, 44 (5):  764-770.  doi: 10.19562/j.chinasae.qcgc.2022.05.014 Abstract ( 235 )   HTML ( 4 )   PDF (2920KB) ( 156 )   Save For investigating the kinematic response and injury differences of riders in different emergent avoidance postures， human finite element model THUMS is used to develop four driving postures， i.e. one normal posture and three emergent avoidance postures： struck side foot landing （SFL）， non-struck side foot landing （NSFL） and landing on both feet （LBF）. A simulation model for a car impacting electric two-wheeler and its rider is built with eight groups of simulations （combination of 20 and 40 km/h two speeds and 4 driving postures） conducted to comparatively analyze the head injury parameters （peak angular acceleration， peak linear acceleration， HIC15， intracranial pressure， CSDM0.15 and MPS） and the Von-Mises stress distribution in lower limbs， and to evaluate the ground impact injury risk of two-wheeler rider. The results show that with a vehicle speed of 20 km/h， the head injury of normal posture rider in ground impact exceeds AIS 4+， while the lower limbs injury of riders with all types of postures do not exceed the fracture threshold. With a vehicle speed of 40 km/h， the head-ground impact injury of riders with all types of postures exceeded AIS 4+， the rider with an emergent avoidance posture of LBF has the highest risk of head injury， the rider with a normal posture has the lowest risk of lower limbs injury， while the rider with an emergent avoidance posture of SFL has a risk of lower limbs fracture. This study clarifies the effects of different emergent avoidance postures on riders' injuries in ground impact and provides an important reference basis for automotive safety technology research. Figures and Tables | References | Related Articles | Metrics

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Evaluation of Driver's Mental Load State Considering the Influence of Noisy Labels Jing Huang,Yang Peng,Ye Huang,Xiaoyan Peng 2022, 44 (5):  771-777.  doi: 10.19562/j.chinasae.qcgc.2022.05.015 Abstract ( 273 )   HTML ( 7 )   PDF (833KB) ( 161 )   Save Most existing studies on driver’s mental load evaluation give the driver’s mental load classification label with the presence or absence of sub-tasks in driving scenes， but drivers may sometime fall into self-thinking， leading to the increase in their mental load in normal driving scenes. In addition， even the same driving sub-tasks may not have the same effects on the mental load of different drivers due to the discrepancy of individuals. As a result， there may exist some noisy labels in the data set collected by traditional methods， hence affecting the training results of the mental load evaluation models of drivers. In view of these problems， the method of confidence learning is adopted to process （detect and filter） the mental load classification labels of drivers in this paper. By using the processed labels， with electroencephalogram， electrocardiogram and skin electricity signal features as model inputs， the driver’s mental load models based on algorithms of support vector machine， random forest， K-near neighbor， decision tree， logic regression， and multi-layer perceptron are constructed to comparatively analyze the effects of noisy label processing on the enhancement of the performance of different models. The results show that after the noise label processing by using confidence learning， the performances of various driver’s mental load models constructed remarkably improve， among which support vector machine model achieves the best results in performance enhancement. Figures and Tables | References | Related Articles | Metrics

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Life Cycle Assessment of City Bus Body Based on Structural Lightweighting Tong Wang,Yiqun Du,Yisong Chen,Rimei Han 2022, 44 (5):  778-788.  doi: 10.19562/j.chinasae.qcgc.2022.05.016 Abstract ( 158 )   HTML ( 5 )   PDF (2686KB) ( 206 )   Save The finite element model for a 12 m monocoque hybrid power city bus is created and the effects of body structure optimization on the energy-saving and emission reduction of the bus are analyzed through strength， stiffness and modal analyses as well as structural lightweighting and life cycle assessment. The results show that compared with original body skeleton， the mass of structurally optimized body skeleton reduces by 52.5 kg and both the strength and stiffness meet the requirements under bending and ultimate torsion conditions with a good natural vibration characteristic. As for the full life cycle， after lightweghting the mining resource consumption reduces by 0.4E04kg Sb-eq.， fossil energy resources consumption reduces by 0.7E04MJ and the overall environment influence factor reduces by 0.42E11， equivalent to a reduction rate of 3.81%， 4.46% and 4.56% respectively. Figures and Tables | References | Related Articles | Metrics

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Lightweight Design of Vehicle Tail-door Inner Panel Made of Injection Molded Short Fiber Reinforced Polymer Composite Zeyang Li,Zhao Liu,Ping Zhu 2022, 44 (5):  789-798.  doi: 10.19562/j.chinasae.qcgc.2022.05.017 Abstract ( 145 )   HTML ( 11 )   PDF (6709KB) ( 229 )   Save In this paper， a lightweight design procedure including the parallel optimization of material and structure is proposed for the inner panel of tail door made of injection-molded short-fiber-reinforced polymer composite in a car. A layered material model is built with consideration of the layered distribution feature of short fiber， on the basis of which a parameterized constitutive model for material is put forward to rapidly predict its mechanical performance when its parameters are changed. An extraction and mapping method for material parameters is proposed according to the distribution features of fiber orientation， so effectively enhancing the accuracy of structural analysis. Considering the design variables of material and structure， combined with Kriging surrogate model and boundary-searching based improved particle swarm optimization algorithm， a lightweight design procedure for composite tail door inner panel is proposed. As a final result， the parallel optimization of material and structure is fulfilled while assuring the design requirements for various working conditions with a lightweighting result of 10.5% mass reduction achieved. Figures and Tables | References | Related Articles | Metrics