Design of tyre force excitation for tyre–road friction estimation

Knowledge of the current tyre–road friction coefficient is essential for future autonomous vehicles. The environmental conditions, and the tyre–road friction in particular, determine both the braking distance and the maximum cornering velocity and thus set the boundaries for the vehicle. Tyre–road friction is difficult to estimate during normal driving due to low levels of tyre force excitation. This problem can be solved by using active tyre force excitation. A torque is added to one or several wheels in the purpose of estimating the tyre–road friction coefficient. Active tyre force excitation provides the opportunity to design the tyre force excitation freely. This study investigates how the tyre force should be applied to minimise the error of the tyre–road friction estimate. The performance of different excitation strategies was found to be dependent on both tyre model choice and noise level. Furthermore, the advantage with using tyre models with more parameters decreased when noise was added to the force and slip ratio.     

The Kalker book of tables for non-Hertzian contact of wheel and rail

A new regularisation of non-elliptical contact patches has been introduced, which enables building the look-up table called by us the Kalker book of tables for non-Hertzian contact (KBTNH), which is a fast creep force generator that can be used by multibody dynamics system simulation programs. The non-elliptical contact patch is regularised by a simple double-elliptical contact region (SDEC). The SDEC region is especially suitable for regularisation of contact patches obtained with approximate non-Hertzian methods for solving the normal contact problem of wheel and rail. The new regularisation is suitable for wheels and rails with any profiles, including worn profiles.

The paper describes the new procedure of regularisation of the non-elliptical contact patch, the structure of the Kalker book of tables, and parameterisation of the independent variables of the tables and creep forces.

A moderate volume Kalker book of tables for SDEC region suitable for simulation of modern running gears has been computed in co-simulation of Matlab and program CONTACT.

To access the creep forces of the Kalker book of tables, the linear interpolation has been applied.

The creep forces obtained from KBTNH have been compared to those obtained by program CONTACT and FASTSIM algorithm. FASTSIM has been applied on both the contact ellipse and the SDEC contact patch. The comparison shows that KBTNH is in good agreement with CONTACT for a wide range of creepage condition and shapes of the contact patch, whereas the use of FASTSIM on the elliptical patch and SDEC may lead to significant deviations from the reference CONTACT solutions.

The computational cost of calling creep forces from KBTNH has been estimated by comparing CPU time of FASTSIM and KBTNH. The KBTNH is 7.8–51 times faster than FASTSIM working on 36–256 discretisation elements, respectively.

In the example of application, the KBTNH has been applied for curving simulations and results compared with those obtained with the creep force generator employing the elliptical regularisation. The results significantly differ, especially in predicted creepages, because the elliptical regularisation neglects generation of the longitudinal creep force by spin creepage.     

基于小波和粒子群算法的HEV行驶状况辨识方法研究

针对混合动力汽车(HEV)行驶状况(道路坡度和整车载荷)变化难以有效识别,导致驱动系统控制策略不能有效满足驾驶员意图问题,以混联式HEV为研究对象,提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型,采用最小二乘法确立了优化目标函数,其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理,最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上,对实车数据进行小波滤波,并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识,并对辨识结果进行小波滤波,结果表明,试验工况下整车载荷辨识的相对误差绝对平均值为2.71%,道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。     

轻型汽油车N_2O排放特性研究

选择1辆满足国Ⅴ排放标准的轻型汽油车作为试验样车,应用全流稀释排放测试系统在FTP75和US06测试循环下,对车辆在不同测试工况下的N_2O排放水平、车辆N_2O排放的影响因素以及N_2O排放随着车辆里程增加的劣化情况进行了研究。研究结果表明:FTP75循环下的N_2O排放结果高于US06循环,前者是后者的4倍左右;在冷起动阶段,由于催化剂没有达到正常的工作温度,车辆会产生较多的N_2O排放,且随着燃油硫含量的增加,车辆的N_2O排放呈现上升的趋势;随着车辆里程的不断增加,车辆的N_2O排放存在一定程度的劣化。     

基于GPS载波相位信号确定运动体姿态的关键技术

应用GPS载波相位信号确定运动体姿态,针对二维两天线系统情况,设计了姿态测量系统的硬件结构及算法流程框图,文章介绍了GPS姿态测量的关键技术：GPS姿态观测方程、整周模糊度的解算、误差的消除。从系统设计角度证明了技术的可行性。     

后轮对独立回转转向架与传统转向架曲线通过性能的对比研究

利用典型的线性稳态模型对传统转向架和新型转向架（后轮对独立回转转向架）的曲线导向能力进行了分析。建立了具有14个自由度的非线性车辆模型,对最佳的一系纵向刚度布置（前导轮对的纵向刚度较小,跟从轮对的刚度较大）的新型转向架和传统转向架车辆的曲线性能进行了仿真对比。仿真结果表明,新型转向架的曲线导向性能优于传统转向架。     

双车道公路危险路段评价指标与安全改善对策

选取相邻路段车速差△V、车速降低系数SRC和速度离差作为鉴别双车道公路危险路段的三个评价指标,从道路工程措施和交通安全设施两方面对事故多发路段进行安全改善对策研究。以内蒙古S203公路为例,利用评价指标进行危险路段判析,针对K465＋900急弯陡坡危险路段,通过线形改造以及设置交通标志标线、减速设施、避险车道和防护设施等一系列工程改造措施,进行安全改善综合方案设计。     

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国家体育场作为北京奥运会的主会场成功地承担了开闭幕式和田径赛事任务。本文研究了其停车场车辆的交通疏散问题,制定了疏散方案。首先,分析了国家体育场停车场和泊位布局及车辆疏散的具体条件。其次,通过给定环境的场景和参数设定,对具体的奥运场馆停车场车辆的疏散方案进行了仿真和评价。最后,利用奥运会开幕式的车辆疏散实测数据进行了对比分析,在仿真中要员和贵宾车辆的总疏散时间为26min,而奥运会开幕式车辆疏散实测结果表明,全部要员、贵宾疏散完毕,共用时27min,从而验证了车辆疏散方案的准确性和实用性。     

基于多模式弱分类器的AdaBoost-Bagging车辆检测算法

针对现有车辆检测算法在实际复杂道路情况下对车辆有效检测率不高的问题,提出了融合多模式弱分类器,并以AdaBoost-Bagging集成为强分类器的车辆检测算法。结合判别式模型善于利用较多的特征形成较好决策边界和生成式模型善于利用较少的特征排除大量负样本的优点,以Haar特征训练判别式弱分类器,以HOG特征训练生成式弱分类器,以AdaBoost算法为桥梁,采用泛化能力强的Bagging学习器集成算法得到AdaBoost-Bagging强分类器,利用Caltech1999数据库和实际道路图像对检测算法进行了验证。验证结果表明:相比于单模式弱分类器,AdaBoostBagging强分类器在分类能力和处理时间上均具有优越性,表现为较高的检测率与较低的误检率,分别为95.7%、0.000 27%,每帧图像的检测时间较少,为25ms;与传统级联AdaBoost分类器相比,AdaBoost-Bagging强分类器虽然增加了12%的检测时间和30%的训练时间,但检测率提升了1.8%,误检率降低了0.000 06%;本文算法的检测性能显著优于基于Haar特征的AdaBoost分类器算法、基于HOG特征的SVM分类器算法、基于HOG特征的DPM分类器算法,具有较佳的车辆检测效果。     

Experimental Investigation of the Effect of Bow Profiles on Resistance of an Underwater Vehicle in Free Surface Motion

In this paper, towing tank experiments are conducted to study the behavior of flow on a model of the underwater vehicle with various shapes of bows, i.e. tango and standard bows in free surface motion ...