车用小排量增压汽油机--传统汽油机动力总成CO2减排的有效方案

降低整车CO2排放是近20年来各大汽车厂商（尤其是欧洲）面临的重要挑战,在采用革新技术的同时,小排量增压汽油机作为传统汽油机动力总成其中也扮演了重要角色。文章着重以德国大众的小排量增压缸内直喷汽油机（TSI技术）为范本,阐述了该项技术在确保优良的整车行驶动力性的同时,实现了整车低燃油消耗;并简述了TSI技术和Ecofuel～CNG的燃料兼容性及降低CO2排放的潜力;越来越多欧洲其它主流轿车品牌也已经开发量产了同类产品,表明该类产品未来若干年仍有增长的市场空间和进一步优化的潜力。     

Eye-Movement Characteristics of Drivers According to Driving Experience-Relevance to Traffic Sign Legibility

An experiment was conducted to find the variability of driver eye movement according to different driving experience.An eye tracking system was used to study the regularity of driver eye movements, such as fixation duration, variations of fixation points, and the distribution of glance zone.It was found that driving experience had a significant effect on driver eye movement behavior.The percentage of fixation duration to total glance time for inexperienced drivers was 61.5%, while the percentage for experienced drivers was 50.2%.Moreover, the majority of drivers paid attention to the left region of the field of view more frequently than the central and the right regions.This study indicates that it takes inexperienced drivers more time to recognize traffic signs.The findings from this study will assist traffic engineers in designing and installing the traffic signs in an optimal way.     

摩托车发动机齿轮传动噪声分析与控制

同样是齿轮传动,由于在发动机中所处的传动部位不同,引起噪声的大小程度也不同。对于换档变速发动机,相互啮合的一对初级传动齿轮副、机油泵传动齿轮副和常啮合式反冲起动是引起噪声的主要部位;对于无级变速发动机,由于传动比变化大,转速变化大,减速齿轮中驱动轴和从动齿轮副是引起噪声的主要部位。     

汽车驱动桥半轴室内试验强化系数模型研究

依据材料疲劳损伤累积理论对汽车驱动桥半轴的疲劳寿命进行预估,结合随机载荷谱处理技术给出了室内试验强化系数的理论模型,研制了汽车驱动桥室内强化试验系统,并对某公司5 t ZL50DWB装载机车桥等多个产品进行了试验。分析表明理论模型和试验数据吻合较好。     

重型车测试工况研究的一种新方法

为了评估车辆尾气排放和燃油经济性。研究重型车测试工况是一项重要的课题。本论文提出了一种新方法用于从速度—时间曲线的行驶数据中提取典型的测试工况。本文介绍了提取这种测试工况的方法,还使用这样的试验工况做了测试尾气排放和燃油消耗的试验,并得出了较好的试验结果。     

电动客车动力系统研究

电动汽车动力系统主要包括动力电池、驱动系统和变速系统。对水平铅酸电池的试验研究显示该电池完全满足电动汽车行驶需要。对新型稀土变磁通电机的驱动特性及其控制原理、线控两挡变速器原理进行了研究,并进行了一体化设计。试验结果表明,装有该动力系统的电动客车动力性和经济性均达到较高的水平。     

A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data

Driver’s stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers’ stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver’s SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers’ behaviors using thousands drivers’ decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply ‘black-box’ procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver’s stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.     

Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities

Accurately estimating driving styles is crucial to designing useful driver assistance systems and vehicle control systems for autonomous driving that match how people drive. This paper presents a novel way to identify driving style not in terms of the durations or frequencies of individual maneuver states, but rather the transition patterns between them to see how they are interrelated. Driving behavior in highway traffic was categorized into 12 maneuver states, based on which 144 (12 × 12) maneuver transition probabilities were obtained. A conditional likelihood maximization method was employed to extract typical maneuver transition patterns that could represent driving style strategies, from the 144 probabilities. Random forest algorithm was adopted to classify driving styles using the selected features. Results showed that transitions concerning five maneuver states – free driving, approaching, near following, constrained left and right lane changes – could be used to classify driving style reliably. Comparisons with traditional methods were presented and discussed in detail to show that transition probabilities between maneuvers were better at predicting driving style than traditional maneuver frequencies in behavioral analysis.     

Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications

The Rakha-Pasumarthy-Adjerid (RPA) car-following model has been demonstrated to successfully replicate empirical driver car-following behavior. However, the validity of this model for fuel consumption and emission (FC/EM) estimation has yet to be studied. This paper attempts to address this research need by analyzing the applicability of the model for FC/EM estimation and comparing its performance to other state-of-practice car-following models; namely, the Gipps, Fritzsche and Wiedemann models. Naturalistic empirical data are employed to generate ground truth car-following events. The model-generated second-by-second Vehicle Specific Power (VSP) distributions for each car-following event are then compared to the empirical distributions. The study demonstrates that the generation of realistic VSP distributions is critical in producing accurate FC/EM estimates and that the RPA model outperforms the other three models in producing realistic vehicle trajectory VSP distributions and robust FC/EM estimates. This study also reveals that the acceleration behavior within a car-following model is one of the major contributors to producing realistic VSP distributions. The study further demonstrates that the use of trip-aggregated results may produce erroneous conclusions given that second-by-second errors may cancel each other out, and that lower VSP distribution errors occasionally result in greater bias in FC/EM estimates given the large deviation of the distribution at high VSP levels. Finally, the results of the study demonstrate the validity of the INTEGRATION micro-simulator, given that it employs the RPA car-following model, in generating realistic VSP distributions, and thus in estimating fuel consumption and emission levels.     

Life cycle of Epischura baicalensis Sars (Copepoda, Calanoida) in Lake Baikal

Epischura baicalensis Sars is a dominant pelagic species of Lake Baikal zooplankton. This is endemic to Lake Baikal and inhabits the entire water column. It produces two generations per year: the winter–spring and the summer. These copepods develop under different ecological conditions and vary in the duration of life stages, reproduction time, maturation of sex products and adult males and females lifespan. The total life period of the animals from each generation is one year. One female can produce 10 egg sacks every 10–20 days during its life time. The ratio of males and females is 1:1. One of the most essential features of the ecology of E. baicalensis is the alteration of its mass inhabited areas during a year, as well as in day time. This is due to the need for various conditions for gonad maturation, reproduction, nourishing and protection from being consumed by planktivors.