2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)最新文献_第2页

Research on Intelligent Diagnosis of Rail Vehicle Transmission System 轨道车辆传动系统智能诊断研究

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661163

Ningguo Qiao, Lin Zhao, Yumei Liu, Qiang Chen, Haijing Hou, Tao Peng

{"title":"Research on Intelligent Diagnosis of Rail Vehicle Transmission System","authors":"Ningguo Qiao, Lin Zhao, Yumei Liu, Qiang Chen, Haijing Hou, Tao Peng","doi":"10.1109/CVCI54083.2021.9661163","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661163","url":null,"abstract":"Rail vehicle transmission system is the key parts of bogies, which transmits load and power. The failures of transmission systems will result in long downtime and expensive maintenance costs. The structure of transmission system is complex and the vibration signals collected from various components are coupled. Therefore, the diagnostic accuracy of a single sensor is relatively low, and fault location is difficult. To improve the accuracy of fault diagnosis, this paper proposes a simple but practicable method based on multiple sensor fusion technology, which combined support vector machine (SVM) with fuzzy integral fusion algorithm (FI). First, the energy entropy features are extracted from multiple sensors data as the inputs of SVMs. Then, the outputs of SVMs are transformed into posteriori probabilities as the basis for calculating fuzzy memberships. Finally, the comprehensive judgment is obtained by FI operation. The data collected from running rail vehicles verify that recognition rate of this scheme is higher than the single sensor and other fusion methods. Moreover, it is also verified that the method has certain application value.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125985205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radar and Camera Fusion based Moving Obstacle Tracking for Automated Vehicles 基于雷达和相机融合的自动驾驶车辆移动障碍物跟踪

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661136

Shihao Wang, Zheng Ma, Ying Li, Chao Yang, Weida Wang, C. Xiang

引用次数: 0

State of health estimation for lithium-ion battery via charging time for partial voltage range 部分电压范围内充电时间对锂离子电池健康状态的影响

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661210

G. Wang, Chunyu Wang, Haitao Yuan, Zhongrui Cui, N. Cui

{"title":"State of health estimation for lithium-ion battery via charging time for partial voltage range","authors":"G. Wang, Chunyu Wang, Haitao Yuan, Zhongrui Cui, N. Cui","doi":"10.1109/CVCI54083.2021.9661210","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661210","url":null,"abstract":"The state of health (SOH) estimation for lithiumion battery is necessary to ensure the reliability and safety of electric vehicles. However, the SOH is related to complex chemical reactions and coupled with multiple physical quantities, it exhibits non-linear characteristics. In this paper, a method based on support vector regression (SVR) and back propagation neural network (BPNN) is proposed to estimate the health state of the battery when the battery is not fully charged and discharged. The length of the charging time in a partial voltage range is selected as the health index. Firstly, the current and voltage data of the battery were obtained by aging cycle test under constant current and constant voltage schedule. Secondly, using Gaussian filter to obtain a smooth IC curve and determine the voltage range where the curve changes dramatically. Thirdly, taking the charging time in the above voltage range as HF and the input of models. The voltage range selected is easily accessible in the pratical application. Results demonstrate that the proposed method provides an accurate SOH estimation.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115806577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Management of Series-Parallel Hybrid Electric Vehicle Considering Engine Torque Tracking and Emissions Optimization 考虑发动机扭矩跟踪和排放优化的串并联混合动力汽车能量管理

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661223

Chaojie Zhu, Xiao Dong, Ping Wang, Hui Zhang

{"title":"Energy Management of Series-Parallel Hybrid Electric Vehicle Considering Engine Torque Tracking and Emissions Optimization","authors":"Chaojie Zhu, Xiao Dong, Ping Wang, Hui Zhang","doi":"10.1109/CVCI54083.2021.9661223","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661223","url":null,"abstract":"Due to different modes of operation, devising a near optimal energy management strategy (EMS) is quite challenging and essential for Series-Parallel Hybrid Electric Vehicle (SPHEV). In addition, The precise control of engine expected torque allocated by EMS is also an inevitable issue, which directly affects the power and stability. Restricted by different time scales between torque split optimization and engine torque control, this paper proposes a bi-level controller to improve the fuel economy, ensure power and reduce emissions of SPHEV. In the outer loop, We first formulate the energy management problem as a nonlinear and constrained optimization control problem. Three different cost functions are defined according to the operation mode, and the power distribution of the internal combustion engine (ICE) and electrical machines is solved at each sample time. In the inner loop, considering the difficulty of establishing the mechanism model of the engine system, which is caused by complexity, strong coupling and nonlinear characteristics, the engine torque control and emission optimization are solved in the framework of data-driven predictive control. The simulation is done on GT-Power and the results indicate that energy efficiency, emission performance and power are improved.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134187023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trajectory tracking control of an ROV using model predictive control considering external disturbances * 考虑外部干扰的ROV轨迹跟踪模型预测控制*

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661139

Chengqi Long, Xiaohui Qin, Yougang Bian, Biao Xu, Manjiang Hu

引用次数: 1

Research on Prediction of State of Charge of Lithium-ion Battery Based on Natural Selection Optimized PSO-SVM Algorithm 基于自然选择优化PSO-SVM算法的锂离子电池电量状态预测研究

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661255

Ran Li, Wenrui Li, Yue Zhang, Kexin Li

{"title":"Research on Prediction of State of Charge of Lithium-ion Battery Based on Natural Selection Optimized PSO-SVM Algorithm","authors":"Ran Li, Wenrui Li, Yue Zhang, Kexin Li","doi":"10.1109/CVCI54083.2021.9661255","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661255","url":null,"abstract":"The state of charge (SOC) of lithium batteries is one of the important performance parameters of electric vehicles, and accurate real-time estimation of SOC can ensure the safe operation of electric vehicles. The traditional particle swarm optimization support vector machine algorithm is effective in predicting small samples. However, as the number of samples increases, there are problems in the prediction of lithium battery SOC of abnormal divergence in the later stage and unstable overall estimation results. To solve the above problems, this paper proposes a support vector machine model based on the natural selection method to improve the particle swarm optimization algorithm to realize the state-of-charge prediction of lithium batteries. The results of the simulation and test demonstrate that the method proposed in this paper can reduce the average relative error of prediction from 2.4% to 1.38%. The algorithm can improve the reliability and stability of the estimation results, and effectively guarantee the safe operation of electric vehicles.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Object Detection and State Estimation of Autonomous Vehicles with Multi-Sensor Information Fusion 基于多传感器信息融合的自动驾驶汽车目标检测与状态估计

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661244

Zheng Li, Yijing Wang, Z. Zuo

引用次数: 1

Realization of Emergency Lane Keeping System by Adaptive Control based on the Finite State Machine 基于有限状态机的应急车道保持系统自适应控制的实现

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661249

Jun Liang, Yanding Yang, Xiaolin Zhu, Kai Sheng

{"title":"Realization of Emergency Lane Keeping System by Adaptive Control based on the Finite State Machine","authors":"Jun Liang, Yanding Yang, Xiaolin Zhu, Kai Sheng","doi":"10.1109/CVCI54083.2021.9661249","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661249","url":null,"abstract":"Emergency lane keeping (ELK) is the function of actively intervening Lane Support Systems, which can avoid or decrease collision risks to other oncoming or overtaking vehicles in the adjacent lane. In this paper, firstly a finite state machine (FSM) is designed to manage the availability and activation states for the ELK system. Then an effective control system, including both the adaptive feedback controller and adaptive feedforward controller, is proposed to ensure the excellent performance of ELK system. By considering the steering wheel angle, yaw velocity, lateral acceleration and other necessary factors in advance, the feedback controller takes heading angle and offset of position as the comprehensive control objective so that the vehicle can drive in the required area with robustness. Based on the road curvature observed by the front camera, the feedforward controller can lead the vehicle to respond the steering demand effectively and quickly even in the case of a sharp turn. Finally, a simulation is provided to prove the effectiveness and benefits of the proposed system for scenarios that defined in the ENCAP for the ELK system.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125820867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pedestrian Detection with YOLOv5 in Autonomous Driving Scenario 自动驾驶场景下YOLOv5的行人检测

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661188

Xianjian Jin, Zhiwei Li, Hang Yang

引用次数: 6

Multi-sensor Fusion Tracking Algorithm by Square Root Cubature Kalman Filter for Intelligent Vehicle 基于平方根立方卡尔曼滤波的智能车辆多传感器融合跟踪算法

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) Pub Date : 2021-10-29 DOI: 10.1109/CVCI54083.2021.9661224

Lin He, Yansong Wang, Qin Shi, Zejia He, Yujiang Wei, Mingwei Wang

{"title":"Multi-sensor Fusion Tracking Algorithm by Square Root Cubature Kalman Filter for Intelligent Vehicle","authors":"Lin He, Yansong Wang, Qin Shi, Zejia He, Yujiang Wei, Mingwei Wang","doi":"10.1109/CVCI54083.2021.9661224","DOIUrl":"https://doi.org/10.1109/CVCI54083.2021.9661224","url":null,"abstract":"With the development of unmanned driving technology, the demand for tracking accuracy is increasing. To rely on a single sensor to obtain detection results in complex environments is limited in accuracy, and multi-sensor fusion is an effective method. Therefore, the environmental sensing technology based on multi-sensor fusion is one of the present research hotspots. This paper proposes a multi-sensor fusion tracking algorithm based on the square root Cubature Kalman filter (SRCKF) for the purpose of nonlinearity of the vehicle target tracking system. This method establishes the equation of state and the measurement equation using the dynamic model, and utilizes the multi sensor measurement signal. A data fusion method based on cubature Kalman filter (CKF) with nonlinear system to avoid errors caused by linearization of nonlinear systems by extended Kalman filter (EKF). In the filtering process, the covariance square root matrix is used in place of the covariance matrix participating in the iterative operation. It effectively avoids the divergence of the filter and improves the convergence speed and stability of the filtering algorithm.","PeriodicalId":419836,"journal":{"name":"2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129700972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3