基于实车行驶工况的电动公交运营期碳足迹测算

针对电动公交车运营期碳足迹难以监测的难题，本文设计了一种以实车行驶工况为基础的电动公交车碳排放折算量估计方法。以西安市主城区的609路和新建城区的362路公交车为研究对象，基于实车行驶轨迹数据提出一种面向城市电动公交运营线路的本地化驾驶工况构建方案。首先，引入T-SNE非线性机器学习算法进行数据降维，使用Birch聚类方法进行分类；然后，根据相似度最高原则和各类别比例关系构建两条线路的电动公交车运行工况。在Cruise仿真环境进行百公里耗电量计算，并折算得到碳排放量。结果表明：609路和362路同车型电动公交车百公里能耗分别为121.71 kW ⋅ h 和144.46 kW ⋅ h ，差异较为明显，证明了分线路进行驾驶工况构建的必要性；基于本文提出的估计方法计算了两条公交线电动公交车组在2019年11月的碳足迹，分别为114.099 t和117.863 t。提出的电动公交运营期碳足迹测算方法有助于推行城市交通碳排放监测与管理。     

电动摩托车续驶里程试验方法研究

续驶里程试验方法高效、合理,可以用较低的试验成本得到较理想的可信度结果,试验仅需一段1500m的试验道路、几百元一台的GPS测速仪和几只标杆,非常方便电动车企业（特别是还没有装备底盘测功机的企业）自行组织试验,以获得第一手试验数据。     

Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.     

Naturalistic driving data collection to investigate into the effects of road geometrics on track behaviour

Road designers assume that drivers will follow the road alignment with trajectories centred in the lane, and move at the design speed parallel to the road centreline (i.e., the horizontal alignment). Therefore, they assume that if the horizontal alignment indicates the “designed trajectory”, the driving path indicates the “operating trajectory”. However, at present, they do not have the necessary tools to measure the relationship between the designed alignment and possible vehicle trajectories.The paper has two objectives: (a) to develop an understanding of the root causes of differences between road alignment and vehicle trajectories; and (b) to define and calibrate a model that estimates the local curvature of trajectories on the basis of the designed horizontal alignment.The two objectives were pursued by carrying out a naturalistic survey using vehicles equipped with high precision GPS in real-time kinematics (RTK) mode driven by test drivers on road sections of known geometric characteristics. The results provide an insight into the effects of road geometrics on driver behaviour, thus anticipating possible driving errors or unexpected/undesired behaviours, information which can then be used to correct possible inconsistencies when making decisions at the design stage.     

纯电动环卫车电驱动系统参数匹配研究

纯电动环卫车已成为城市日常生活的重要组成部分,为提高其动力性及作业效率,文章以一种基于平行轴变速箱与行星排集成的多模高效电驱动系统构型的纯电动环卫车为研究对象,首先对系统能够实现的模式进行了分析,其次以纯电动环卫车整车参数和动力性能为指标,分别匹配了驱动电机和作业电机峰值、额定功率、转矩及最高转速等参数,并搭建了整车动力学模型,通过仿真分析及系统台架进行爬坡和加速能力测试,证明系统参数匹配的有效性。     

A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications

The fact that electric vehicles (EVs) are characterized by relatively short driving range not only signifies the importance of routing applications to compute energy efficient or optimal paths, but also underlines the necessity for realistic simulation models to estimate the energy consumption of EVs. To this end, the present paper introduces an accurate yet computationally efficient energy consumption model for EVs, based on generic high-level specifications and technical characteristics. The proposed model employs a dynamic approach to simulate the energy recuperation capability of the EV and takes into account motor overload conditions to represent the vehicle performance over highly demanding route sections. To validate the simulation model developed in this work, its output over nine typical driving cycles is compared to that of the Future Automotive Systems Technology Simulator (FASTSim), which is a simulation tool tested on the basis of real-world data from existing vehicles. The validation results show that the mean absolute error (MAE) of cumulative energy consumption is less than 45 W h on average, while the computation time to perform each driving cycle is of the order of tens of milliseconds, indicating that the developed model strikes a reasonable balance between efficacy of representation and computational efficiency. Comprehensive simulation results are presented in order to exemplify the key features of the model and analyze its output under specific highly aggressive driving cycles for road gradients ranging from −6% to 6%, in support of its usability as a practical solution for estimating the energy consumption in EV routing applications.     

The design of an in-vehicle assistance system to support eco-driving

This driving simulator study was the second of two studies investigating the most effective and acceptable in-vehicle system for the provision of guidance on fuel efficient accelerator usage. Three eco-driving interfaces were selected for test (a second-order display visual display with auditory alerts and two haptic accelerator pedal systems) following a pilot study of 12 different interfaces. These systems were tested in a range of eco-driving scenarios involving acceleration, deceleration and speed maintenance, and assessed through their effects on fuel economy, vehicle control, distraction, and driver subjective feedback. The results suggest that a haptic accelerator pedal system is most effective for preventing over-acceleration, whilst minimal differences were observed between systems in terms of the effect of the assistance provided to prevent under-acceleration. The visual–auditory interface lowered the time spent looking towards the road, indicating a potential negative impact on driver safety from using this modality to provide continuous green driving support. Subjective results were consistent with the objective findings, with haptic pedal systems creating lower perceived workload than a visual–auditory interface. Driver acceptability ratings suggested a slight favouring of a haptic-force system for its usefulness, whereas the more subtle haptic-stiffness system was judged more acceptable to use. These findings offer suggestions for the design of a user-friendly, eco-driving device that can help drivers improve their fuel economy, specifically through the provision of real-time guidance on the manipulation of the accelerator pedal position.     

Development of a driving simulator based eco-driving support system

This research developed an eco-driving feedback system based on a driving simulator to support eco-driving training. This support system could provide both dynamic and static feedback to improve drivers’ eco-driving behavior. In the process of driving, drivers could get voice prompts (e.g., please avoid accelerating rapidly) once non-eco-driving behavior appeared, and also could see the real-time CO₂ emissions curves. After driving, drivers could receive an eco-driving evaluation report including their fuel consumption rank, potential of fuel saving and driving advice corresponding to their driving behavior. In this support system, five items of non-eco-driving behavior (i.e., quick accelerate, rapid decelerate, engine revolutions at a high level, too fast or unstable speed on freeways and idling for a longer time) were defined and could be detected. To validate this support system’s effectiveness in reducing fuel consumption and emissions, 22 participants were recruited and three driving tests were conducted, first without using the support system, then static feedback and then dynamic feedback utilized respectively. A reduction of 5.37% for CO₂ emissions and 5.45% for fuel consumption was obtained. The results indicated that the developed eco-driving support system was an effective training tool to improve drivers’ eco-driving behavior in reducing emissions and fuel consumption.     

Are multi-car households better suited for battery electric vehicles? – Driving patterns and economics in Sweden and Germany

Battery electric vehicles (BEVs) could reduce CO₂ emissions from the transport sector but their limited electric driving range diminishes their utility to users. The effect of the limited driving range can be reduced in multi-car households where users could choose between a BEV and a conventional car for long-distance travel. However, to what extent the driving patterns of different cars in a multi-car household’s suit the characteristics of a BEV needs further analysis. In this paper we analyse the probability of daily driving above a fixed threshold for conventional cars in current Swedish and German car driving data. We find second cars in multi-car households to require less adaptation and to be better suited for BEV adoption compared to first cars in multi-car households as well as to cars in single-car households. Specifically, the share of second cars that could fulfil all their driving is 20 percentage points higher compared to first cars and cars from single-car households. This result is stable against variation of driving range and of the tolerated number of days requiring adaptation. Furthermore, the range needed to cover all driving needs for about 70% of the vehicles is only 220 km for second cars compared to 390 km for the average car. We can further confirm that second cars have higher market viability from a total cost of ownership perspective. Here, the second cars achieve a 10 percentage points higher market share compared to first cars, and to cars in single-car households for Swedish economic conditions, while for Germany the corresponding figure is 2 percentage points. Our results are important for understanding the market viability of current and near-future BEVs.     

A real-time system for monitoring driver fatigue

ABSTRACT

This paper presents a nonintrusive prototype computer vision system for real-time fatigue driving detection. First, we use Haar-like features to detect a driver’s face and conduct tracking by introducing an improved Camshift algorithm. Second, we propose a new eye-detection algorithm that combines the Adaboost algorithm with template matching to reduce computational costs and add an eye-validation process to increase the accuracy of the detection rate. Third, and different from other methods focusing on detecting eyes using the ‘bright pupil’ effect, which only works well only for certain constrained lighting conditions, our method detects and estimates the iris center in the hue (H) channel of the hue, saturation, value color space and fits the iris with an ellipse. After extracting the eye fatigue features, we calculate the PERCLOS measurement for fatigue evaluation. This system has been tested on the IMM Face Database, which contains more than 200 faces, and in a real-time test. The experimental results show that the system possesses good accuracy and robustness.