Table of Content

25 November 2021, Volume 43 Issue 11 Previous Issue    Next Issue

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Potential Conflict Analysis and Risk Quantification Method of Intelligent Vehicle Lane Change Jiqing Chen,Chubin Weng,Fengchong Lan 2021, 43 (11):  1565-1576.  doi: 10.19562/j.chinasae.qcgc.2021.11.001 Abstract ( 410 )   HTML ( 41 )   PDF (2985KB) ( 733 )   Save In the absence of effective real lane change accident data， the potential conflict forms of vehicles with surrounding vehicles at different stages of complete lane change process are analyzed based on traffic conflict theory. Through the derivation of microscopic conflict risk indicators， the extraction of macroscopic lane change risk features and the analysis on systematic lane change risk， a comprehensive quantification method of lane change risk is established with concurrent considerations on the possibility and severity of potential collisions. An application test is conducted to analyze the comprehensive risk quantification method using the natural driving trajectory data set. The results show that the quantification method proposed can objectively and reasonably reflect the risk features of lane change of different types and locations， providing a new idea for the decision-making and planning of safe lane change of intelligent vehicles. Figures and Tables | References | Related Articles | Metrics

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Cooperative Optimization Control Method of Traffic Signals and Vehicle Trajectories at Connected Intersection Xiangmo Zhao,Xinrui Zhang,Runmin Wang,Zhigang Xu,Haijin Fan 2021, 43 (11):  1577-1586.  doi: 10.19562/j.chinasae.qcgc.2021.11.002 Abstract ( 342 )   HTML ( 35 )   PDF (2180KB) ( 451 )   Save In order to improve the traffic efficiency and fuel economy at the connected intersection， a collaborative control application scenario of the connected and signalized intersection is designed， and a hierarchical decoupling collaborative optimization control method of traffic signals and vehicle trajectories is proposed. An optimization method combining “job scheduling” and genetic algorithm is used to minimize the average travel time delay at the top layer to realize signal timing and phase sequence optimization； at the bottom layer， combined with the timing optimization result， the piecewise optimization method is used to realize vehicle trajectories optimization so as to minimize the average fuel consumption. The simulation results show that the proposed cooperative control method can effectively improve the traffic efficiency and fuel economy at the connected intersection under the conditions of balanced and unbalanced traffic flow， and low and high traffic density. The effect of the proposed method is more significant with the decrease of minimum green light duration and increase of V2X communication range. Considering the factors of traffic fairness and construction cost， the minimum green light duration and effective V2X communication range of the proposed cooperative control system for connected signalized intersection should be set as 5 s and 800 m respectively. Figures and Tables | References | Related Articles | Metrics

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Research on Vehicle Multi-Target Environment Aware Tracking Algorithm Based on Self-Query Long Chen,Chengzheng Zhu,Yingfeng Cai,Hai Wang,Yicheng Li 2021, 43 (11):  1587-1593.  doi: 10.19562/j.chinasae.qcgc.2021.11.003 Abstract ( 259 )   HTML ( 26 )   PDF (2860KB) ( 329 )   Save In order to balance the tracking performance （i.e. the indicators of MOTA， MOTP and IDSW etc.） and tracking speed， especially， to solve the complexity of the post processing for video multi-target tracking， a multi-target tracking method based on autoregressive query mechanism is proposed， with training and verification conducted. The results of verification show that the inference of each frame of picture takes about 44 ms， and the accuracy of multi-target tracking reaches 58.9%. The model is integrated into the ROS platform of intelligent vehicle for testing and the results of test indicate that the algorithm proposed can achieve multi-target real-time tracking in complex traffic scenes， and the algorithm has good practical application value. Figures and Tables | References | Related Articles | Metrics

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Roughness Measure-Based Road Drivable Region Identification Lei Ma,Zebin Liu,Biao Cao,Wenju Wang,Quanyuan Qiu 2021, 43 (11):  1594-1601.  doi: 10.19562/j.chinasae.qcgc.2021.11.004 Abstract ( 159 )   HTML ( 13 )   PDF (4333KB) ( 235 )   Save A method for identifying the drivable region of road image based on roughness measure is proposed in this paper. Firstly， the roughness information of the road image is defined to obtain the color distribution histogram of the road image and its upper and lower approximation information， and the initial segmentation of the road image is realized through the roughness measurement. Then， region merger is conducted according to the color difference and pixel scale in the color regions of the initial segmentation image， to obtain the characteristics of the drivable regions of the road. Next， the improved region growing algorithm is used to identify the feature image. Finally， the recognition of the drivable regions of the road is realized. The results of experiment show that the identification method has achieved good results. Figures and Tables | References | Related Articles | Metrics

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Multi-level and Multi-modal Target Detection Based on Feature Fusion Teng Cheng,Lei Sun,Dengchao Hou,Qin Shi,Junning Zhang,Jiong Chen,He Huang 2021, 43 (11):  1602-1610.  doi: 10.19562/j.chinasae.qcgc.2021.11.005 Abstract ( 372 )   HTML ( 15 )   PDF (6453KB) ( 389 )   Save For the problems of low robustness of the environment perception and identification difficulty of small targets of autonomous driving in complex environment， a multi-level and multi-modal fusion method based on feature fusion is proposed in this paper. Firstly， the image and point cloud modal information are mapped to the same dimension， and the hierarchical features of different size targets are extracted. On this basis， the multi-modal multi-level feature fusion is carried out. Then， six comparative experiments are designed to verify the effectiveness of each module. Finally， the Waymo data set and NIO real car data are used for training and testing. The test results show that the detection MAP value of the network is improved by 23.1% compared with that of YOLO V3. Figures and Tables | References | Related Articles | Metrics

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Obstacle Tracking of a Lidar-equipped Vehicle in Turning for Collision Avoidance Zhiguo Zhao,Peng Wang,Xiaorong Chen,Kaichong Liang 2021, 43 (11):  1611-1619.  doi: 10.19562/j.chinasae.qcgc.2021.11.006 Abstract ( 189 )   HTML ( 17 )   PDF (4740KB) ( 335 )   Save At present， the perception and positioning of surrounding obstacles by onboard lidarare mostly based on vehicle coordinate system. When the vehicle turns to avoid collision， due to the change of heading angle and the rotation of vehicle coordinate system， the difficulties of the obstacle data correlation， the analysis of surrounding vehicle motion state and the path planning for collision avoidance will increase. In order to eliminate the adverse effects of vehicle steering， atarget obstacle tracking methodis proposed based on the transformation of onboardlidar coordinate system. Firstly， the improved K-means clustering algorithm is used to cluster the road boundary points extracted to fit the road boundary line.Then， sparrow search algorithmisadopted to solve out the heading angle of vehicle accordingtoroad boundary， with the lidar coordinate system transformed. Finally， the association algorithm and particle filter are used to track the target obstacles. The results of real vehicle test show that the algorithm proposed can accurately extract the road boundary and track obstacles in vehicle turning for collision avoidance. Figures and Tables | References | Related Articles | Metrics

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Research on Skilled Driver's Trajectory Fitting Based on Improved ELM Xinwei Jiang,Long Chen,Yiding Hua,Xing Xu 2021, 43 (11):  1620-1630.  doi: 10.19562/j.chinasae.qcgc.2021.11.007 Abstract ( 166 )   HTML ( 10 )   PDF (2876KB) ( 207 )   Save In order to make the steering control level of the intelligent cars as close as possible to the level of human drivers， a nonlinear fitting method for training and learning the trajectory of skilled drivers is proposed. Based on the piecewise polynomial method， the expression models of four typical steering conditions including right turn， U-turn， lane keeping and lane change are constructed， and the adaptive pseudo-spectral method is used to realize effective connection of piecewise trajectories. In order to avoid the problem of the traditional neural network learning algorithm （such as BPNN） that it is necessary to artificially set a large number of network training parameters， and it is easy to produce the local optimal solution， a nonlinear skilled driver's driving trajectory fitting strategy based on improved extreme learning machine （ELM） is proposed. The Kalman filter （KF） algorithm is introduced to filter the ELM output weight matrix， update the stage cycle calculation， optimize the ELM algorithm， and improve the learning accuracy of ELM in multi-collinearity. KFELM， ELM and BPNN are used respectively to perform nonlinear fitting tests on the skilled driver's driving trajectory under different working conditions. The results show that the training precision and test accuracy of KFELM are obviously better than ELM and BPNN， and the learning speed of KFELM is slightly better than that of ELM， and significantly better than that of BPNN. The improved ELM driving model training method provides a theoretical basis for decision control for autonomous vehicles. Figures and Tables | References | Related Articles | Metrics

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Combustion Optimization for Jet Ignition Direct Injection Gasoline Engine Ziqing Zhao,Yunliang Qi,Ning Tian,Yi Zhang,Cong Liu,Yi Yao,Zhi Wang 2021, 43 (11):  1631-1637.  doi: 10.19562/j.chinasae.qcgc.2021.11.008 Abstract ( 218 )   HTML ( 8 )   PDF (2284KB) ( 270 )   Save Jet ignition technology is an effective way to achieve lean burn and improve thermal efficiency of the engine. In this paper， a jet igniter is installed on a multi-cylinder gasoline engine to carry out jet ignition combustion optimization through different injection strategies. Firstly， the minimum fuel consumption operation point is determined within the high efficiency range of the original engine working with conventional spark ignition. Then， based on the selected working condition， the effect of single and dual injection strategies on fuel consumption of jet ignition is studied， and key parameters of dual injection strategy are optimized. Finally， the combustion and emission characteristics of different injection strategies and ignition methods are further compared. The results show that compared with spark ignition the fuel consumption of the engine is reduced by jet ignition， especially when combined with dual injection strategy during the intake stroke， with the minimum fuel consumption rate of the engine reduced by 4 g·（kW·h）-1. The emission of CO and THC of jet ignition is basically the same as that of spark ignition， but NOx emission is higher. Compared with single injection strategy， using dual injection strategy can result in lower NOx emission. Figures and Tables | References | Related Articles | Metrics

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Effect of PODE Mixing on Emission Characteristics of Hybrid Diesel Engines Under Transient Conditions Dongdong Chen,Tie Wang,Guoxing Li,Tianyou Qiao,Zhenning Hou 2021, 43 (11):  1638-1644.  doi: 10.19562/j.chinasae.qcgc.2021.11.009 Abstract ( 128 )   HTML ( 6 )   PDF (2318KB) ( 339 )   Save In order to study the effect of Polyoxymethylene ether （PODE） mixing on the transient emissions of hybrid diesel engines， based on a single-shaft parallel hybrid test platform， this paper uses 0# diesel and diesel/PODE fuel mixture with PODE in volume ratios of 10% and 20% respectively. The test transient conditions are that the diesel engine speed is 1 900 r/min and the throttle changes from 25% to 35% with the transient working conditions of 0， 2 and 5 s transition time and 800 r/min drag dynamic quick start conditions. The results show that under constant speed and variable throttle conditions， as the transition time decreases， the cumulative NOx and Soot emission peaks during the change process increase. With the increase of the PODE ratio， the transient distortion of Soot emission first increases and then decreases. Compared with burning 0# diesel， when burning diesel/PODE fuel mixture， the cumulative NOx emission value during the transient process increases， but the Soot emission peak value drops significantly. When the transition time is 0 s， the Soot emission peak value is reduced by 37% and 63% respectively. When the transition time is 2 s， the peak Soot emission is reduced by 31% and 59%， respectively； when the transition time is 5 s， the peak Soot emission is reduced by 34% and 57%， respectively. Under fast starting conditions， compared to burning 0# diesel， when burning diesel/PODE fuel mixture， the peak CO emission during the starting process is reduced by 10% and 26% respectively， and the peak Soot emission is reduced by 53% and 60% . The change of NOx emission is not significant. Figures and Tables | References | Related Articles | Metrics

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Study on Uncertainty of Vehicle Wind Noise Performance Under Dynamic Seal Condition Zuofeng Pan,Yuwei Deng,Yaodong Hao,Lili Su,Jianghua Deng 2021, 43 (11):  1645-1652.  doi: 10.19562/j.chinasae.qcgc.2021.11.010 Abstract ( 144 )   HTML ( 8 )   PDF (3573KB) ( 242 )   Save The analysis and optimization methods of interval uncertainty of vehicle interior wind noise performance under dynamic seal condition are proposed in this paper. Firstly， the statistical energy model of the vehicle is established， the wind noise loads after spectrum decomposition are applied onto the model to complete the calculation of interior wind noise. Then， the transmission losses of sealing strips under different compression conditions are measured， and the upper and lower bounds of the transmission losses of sealing strips are calculated according to the change of the compression amounts of sealing strips during vehicle driving to fulfill the description of uncertain variables. Finally， based on interval perturbation theory， the variation range of interior wind noise pressure level is analyzed， a robust optimization model is established， and the central value and the radius of the perturbation interval of wind noise pressure level are optimized. The results of calculation examples show that the method proposed can reduce the interior noise level and its fluctuation amplitude on the premise of ensuring the mass of relevant parts basically unchanged， apparently enhancing the robustness of the system. Figures and Tables | References | Related Articles | Metrics

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Research on the Influences of Side-Wing Diffuser on Automotive Aerodynamic Characteristics Hong Liao,Yaohui Lu,Qingyang Wang,Xiaochun Yin,Xiaobo Shi,Yanhui Tang 2021, 43 (11):  1653-1661.  doi: 10.19562/j.chinasae.qcgc.2021.11.011 Abstract ( 166 )   HTML ( 3 )   PDF (5143KB) ( 303 )   Save In order to study the influences of the side-wing diffuser on the aerodynamic characteristics of a car， a side-wing diffuser is installed on the CAERI Aero Model， and a computational fluid dynamics （CFD） software is used to simulate the aerodynamic drag and lift force of vehicle with diffuser installed at different angles， and compared with general plane diffuser， in which the simulation result of the plane diffuser with a certain installation angle has been verified by wind tunnel test to conform the reliability of the simulation. The results of simulation show that the side-wing diffuser can change the distribution of vortex structure at the bottom and tail of the vehicle. Compared with the plane diffuser， the side-wing diffuser can effectively reduce the aerodynamic lift of the vehicle at different installation angles of diffuser， with the maximum declining rate of aerodynamic lift force reaching 38.1% at an installation angle α = 10.5°. The side-wing diffuser can greatly reduce the aerodynamic lift with a trivial increase of aerodynamic drag， effectively enhancing the driving stability and safety of vehicle. Figures and Tables | References | Related Articles | Metrics

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A Spatio-Temporal Prediction Method of Traffic Flow Based on Multi-Source Data Jie Hu,Yongsheng Gong,Shijie Cai,Tengfei Huang 2021, 43 (11):  1662-1672.  doi: 10.19562/j.chinasae.qcgc.2021.11.012 Abstract ( 162 )   HTML ( 9 )   PDF (4503KB) ( 363 )   Save In order to enhance the accuracy of traffic flow prediction， a traffic flow prediction method based on multi-source data and spatio-temporal prediction is proposed in this paper with comprehensive considerations of the effects of various factors on traffic flow from the perspectives of external features， time features and spatial features. In terms of external features， the influence of the features of date， weather and point of interest on traffic flow is explored in depth. In terms of time features， a time series prediction framework based on time convolution network is put forward， and the time prediction models are established with the neighbor cycle and daily cycle as base lines respectively. In terms of spatial features， a spatial feature extraction method based on graph representation learning is proposed to fulfill the extraction of the spatial correlation feature between adjacent road network nodes. The results show that compared with various existing prediction methods， the method adopted improves the prediction performance of medium- and long-term traffic flow with high prediction accuracy. Figures and Tables | References | Related Articles | Metrics

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Research on Decoupling Control of Integrated Dynamics System of Unmanned Vehicle Chassis Yutian Zhang,Feiran Li,Hanqing Tian,Jibin Hu,Chao Wei,Wei Wu 2021, 43 (11):  1673-1682.  doi: 10.19562/j.chinasae.qcgc.2021.11.013 Abstract ( 288 )   HTML ( 15 )   PDF (4869KB) ( 558 )   Save This paper proposes and verifies a decoupling controller of chassis dynamics integrated control system for the multi wheel distributed drive all-wheel steering unmanned vehicle， in order to further improve the dynamic performance of flexible， fast and accurate unmanned platform through decoupling control. Firstly， a vehicle coupling dynamic model is established which can accurately reflect the longitudinal， lateral and yaw motion of the vehicle， and the input-output coupling characteristics of the dynamic system are quantitatively analyzed combined with the nonparametric statistical method. Then， based on the principle of Neural Network Inverse （NNI） system， the vehicle dynamic system with decoupling linearization and decoupling composite controller are constructed. The decoupling response of the system is tested and verified successfully， and the proposed controller is verified by experiments. The results show that the interference caused by the dynamic coupling relationship between the control sub-channels of each degree of freedom of the coupling dynamic system is effectively reduced， so that relatively independent control of each sub-channel can be realized， which lays an important platform and physical foundation for efficient， accurate and stable comprehensive dynamic performance in the trajectory tracking process of distributed drive unmanned vehicle. Figures and Tables | References | Related Articles | Metrics

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Braking Response Time Prediction Model Based on Multi-dimensional Driving Characteristics Baicang Guo,Xianyi Xie,Lisheng Jin,Hui Rong,Yang He,Bingdong Ji 2021, 43 (11):  1683-1692.  doi: 10.19562/j.chinasae.qcgc.2021.11.014 Abstract ( 172 )   HTML ( 3 )   PDF (3092KB) ( 288 )   Save In order to accurately predict the driver's braking reaction time （BRT）， a BRT prediction model based on the characteristics of differentiated drivers is built. The experiment is designed with multi task driving behavior as the inducing factors of differentiated driver characteristics， then the real vehicle experiment is carried out on closed urban road and the BRT data is collected. The drivers' multi-dimensional driving characteristics variable data is obtained by the self-reported information collection method. The structural equation model （SEM） is used to deconstruct the influencing factors of BRT and the path coefficients are used to optimize the weights of back propagation neural network （BPNN）. Finally， a prediction model of BRT based on SEM-BPNN is established. The verification and test results show that the overall regression R value of the proposed BRT prediction model is greater than 0.9 and the total error is 0.032 4. It has better prediction accuracy and fitting performance， moreover， it can reduce the problem of poor robustness caused by unstable network convergence while considering the multi-dimensional characteristics of drivers. Figures and Tables | References | Related Articles | Metrics

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Adaptive Energy Management Strategy for High Power Hydrogen Fuel Cell Heavy-duty Truck Based on Low Pass Filter Ruiliang Zhang,Zhun Chen,Senhai Liu,Zhengwu Fan 2021, 43 (11):  1693-1701.  doi: 10.19562/j.chinasae.qcgc.2021.11.015 Abstract ( 171 )   HTML ( 8 )   PDF (2186KB) ( 439 )   Save In view of the short service life of hydrogen fuel cell caused by frequent change of load， taking a heavy truck equipped with high power hydrogen fuel cell as the object of study， an adaptive energy management strategy based on low-pass filter is proposed according to the characteristics of its hybrid power system components and the frequency domain characteristics of vehicle desired power under typical working conditions. The strategy， together with adaptive low-pass filter and logic rules， fulfill the rational allocation of whole vehicle energy， and for giving full play to the role of adaptive low-pass filter， on which a multi-objective optimization is conducted by using Pareto genetic algorithm. A simulation on the model for a heavy truck with high power hydrogen fuel cell built with Matlab/Simulink is carried out and its results show that compared with the traditional power following energy management strategy， the energy management strategy proposed can effectively reduce the output power fluctuation of hydrogen fuel cell system on the premise of ensuring the power performance and fuel economy of heavy truck with high-power hydrogen fuel cell， and an optimization can further reduce the output power fluctuation of fuel cell system by 9.28%， conducive to the enhancement of the durability of hydrogen fuel cell. Figures and Tables | References | Related Articles | Metrics

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Short-circuit Current Estimation of Battery Pack Based on Average- difference Model Li Zhang,Xingqi Gao,Jianhao Zhang 2021, 43 (11):  1702-1709.  doi: 10.19562/j.chinasae.qcgc.2021.11.016 Abstract ( 135 )   HTML ( 4 )   PDF (2353KB) ( 304 )   Save In view of the difficulty in rapidly and effectively detecting the internal short circuitof battery which is the key link in triggering thermal runaway， a short-circuit current estimation method forseries battery pack suitable for different short circuit resistances is proposed in this paper based on the average-difference model. Firstly， the applicability of the average-difference modelfor short-circuit battery and its parameter correction method are analyzed， and the model parameters are identified online by using iterative least squares and Kalman filtering. Then the short-circuit current estimation expression based on model parameters and battery terminal voltage， and the relationship between the instantaneous voltage drop and the short-circuit resistance are derived， and the way of short-circuit current estimation are divided into long-term and short term two methods according to whether there is an obvious instantaneous voltage drop. Finally， the results of verification experiments show that the method proposed can estimate short-circuit current more accurately， and is suitable for short-circuit detection with different resistance values. Figures and Tables | References | Related Articles | Metrics

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SOH Estimation Method for Lithium-ion Batteries Based on DTV-IGPR Model Ping Wang,Xiangyuan Peng,Ze Cheng 2021, 43 (11):  1710-1719.  doi: 10.19562/j.chinasae.qcgc.2021.11.017 Abstract ( 222 )   HTML ( 8 )   PDF (5191KB) ( 297 )   Save The state of health （SOH） of lithium-ion batteries is a key factor to ensure the safe and reliable operation of electric vehicles. The existing SOH estimation methods usually ignore the temperature information that can characterize battery aging in the process of capacity degradation. In view of this， this paper proposes a method to obtain the differential temperature voltammetry （DTV） curve based on the battery surface temperature and a filtering method combining moving average （MA） and Kalman filtering （KF） to extract the health feature. In addition， the combined kernel function is used to improve the traditional Gaussian process regression （GPR） algorithm to fit the two trends of overall decline and local fluctuations of battery capacity， so as to establish a DTV-IGPR battery-aging model for SOH estimation. Single cell and multi cell verification are carried out using the Oxford and NASA datasets， which are collected at two different ambient temperatures. The results show that the proposed method has high SOH estimation accuracy and strong robustness. Figures and Tables | References | Related Articles | Metrics

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Review of State Estimation of Lithium-ion Battery with Machine Learning Yizhan Xie,Ximing Cheng 2021, 43 (11):  1720-1729.  doi: 10.19562/j.chinasae.qcgc.2021.11.018 Abstract ( 684 )   HTML ( 36 )   PDF (464KB) ( 742 )   Save This paper aims to give a comprehensive review on the research progress in the field of the estimation of the states of lithium-ion battery， including the state of charge （SOC）， state of health （SOH） and residual useful life （RUL）. Firstly， the application status of machine learning method to the estimation of battery states are expounded. Then， five specific implemental links of machine learning methods for battery state estimation are summarized， including data preparation， model selection and evaluation， hyperparameter determination， data preprocessing and model training， and an evaluation method of learning algorithms is proposed in terms of fusion accuracy， implementation cost and robustness. Finally， the problem of scene adaptability in determining hyperparameters is pointed out， with a suggestion put forward： establishing multi-regional， cross-seasonal， multi-mode and long-term driving cycle database of traction battery， so as to promote the research on the practicability and universality of machine learning algorithms for battery state estimation. References | Related Articles | Metrics

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Study on the Collision of Battery Pack Bottom Based on Traffic Accident Statistics Yue Wang,Pengcheng Xin,Dayong Zhou,Gang Li,Li Tang,Pengxiang Wang 2021, 43 (11):  1730-1735.  doi: 10.19562/j.chinasae.qcgc.2021.11.019 Abstract ( 262 )   HTML ( 11 )   PDF (3157KB) ( 460 )   Save In order to reduce the potential fire risk caused by the collision on the bottom of battery pack in new energy vehicle，the typical conditions of upward collision onbattery bottom are summarized，including upward collision on battery bottom and horizontal collision based on statistical analysis on traffic accidents. A finite element model for the collision analysis on battery bottom is built with simulation on different conditions conducted. The results show that in the condition of upward collision， the damage of battery pack is mainly caused by the action of speed shock alone Z direction. Among them， cone-shaped ground obstaclecan directly lead to the failure of battery bottom， in the condition without housing failure， semi-spherical ground obstacle will cause severer damage， and in the condition of horizontal collision， the determination of the position of minimumground clearance and the design of the front protection structure of battery pack have significant influences. Figures and Tables | References | Related Articles | Metrics