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.     

直线式液压马达偏心轴的设计与分析

介绍一种直线式液压马达设计方案,首先对偏心轴进行受力分析,以确定偏心轴的主要结构参数,然后使用Solidworks进行三维建模,最后使用ANSYS进行强度分析,为偏心轴的研究提供理论基础。     

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.     

湛江霞山港区深厚老沉积土地基钢管桩承载力试验分析

通过深厚老沉积土地区钢管桩的压桩试验、拔桩试验和桩身轴力试验,探讨了深厚老沉积土地区钢管桩压桩和拔桩的土阻力分布规律,得出如下结论:深厚老沉积土地区钢管桩压桩状态下侧阻力均值为76.79 MPa,比一般黏性土高27.5%~30.6%;老沉积土拔桩状态下的侧阻力值达到42.4~52.8 MPa;老沉积土拔桩侧阻力和压桩侧阻力的比值均值为0.84,比一般黏性土值要高5%;老沉积土压桩和拔桩侧阻力较为恒定,随着土层埋深和土层厚度的变化较小。这对深厚老沉积土地基中钢管桩的理论研究和工程应用具有指导作用。     

机车车轴超声自动检测系统设计

针对机车车轴缺陷复杂,检测难度大,目前手工超声检测效率低、可靠性差的现状,设计一项机车车轴超声自动检测系统。该系统的设计基于对车轴结构和缺陷的分析,并从机械设计模块、超声探伤模块和控制及软件设计模块3方面进行研究。设计完成的系统采用螺旋轨迹扫查方式,由6组探头同时扫查车轴面,实现了缺陷的判定与显示、自动存储检测结果和生成检测报告等功能。与手工检测方式相比,该系统极大提高了车轴检测的效率和可靠性。     

双中间轴式手动变速器齿轮敲击噪声理论及试验研究

以单对齿轮副为例,建立了其非线性动力学模型,详细阐述了齿轮敲击噪声产生的机理及控制措施。结合某双中间轴式手动变速器出现的齿轮敲击噪声问题,运用AMESim软件建立了多对齿轮副的动力学模型,通过台架及整车试验,分析了该变速器齿轮敲击噪声产生的关键参数并提出了改进措施,为变速器开发提供了控制依据。     

应变测量在轴系扭转振动测试中的应用

针对目前船舶轴系扭转振动测试主要采用的角位移和角速度测量方法不能直接测量出轴系扭转振动应力,会造成一定程度的误差的问题,基于目前非常成熟的应变测量技术,对轴系扭转振动的测量进行分析,给出数据处理的方法,并提出基于无线遥测轴功率系统进行功率-扭振测试系统的开发和数据分析方法。