Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

Virtual 3D city model as a priori information source for vehicle localization system

This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on–board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches.     

The impact of car specifications,prices and incentives for battery electric vehicles in Norway: Choices of heterogeneous consumers

Electric vehicles (EVs), specifically Battery EVs (BEVs), can offer significant energy and emission savings over internal combustion engine vehicles. Norway has a long history of research and government incentives for BEVs. The BEV market in Norway allows us to fully examine consumers’ BEV choices influenced by car specifications, prices and government incentives (public bus lanes access, toll waiver and charging stations). The Random-Coefficient Discrete Choice Model (referred to as the BLP model) is applied to understand the choices of heterogeneous personal consumers and business buyers. Our study is instantiated on the entire EV sales data in Norway from 2011 to 2013, as well as a set of demographics at the municipality level. The results suggest significant positive effects of BEV technology improvement, space, toll waiver and charging station density on EV demand for both personal consumers and business buyers. However, the effects on business buyers may be generally less pronounced than on personal consumers. Interestingly, bus lanes access demonstrates a negative impact for personal consumers, possibly due to consumers’ concern regarding bus lane congestion. In addition, preferences on the BEV price can vary statistically among consumers with different income levels. Compared to the BEV technology development, demographical features and municipal incentives may have generally less impacts on market shares within the BEV market.     

带时间窗VRP问题的多智能体进化算法

基于实用性和合理性的角度, 研究了单个配送中心带时间窗的车辆路径问题。以行驶时间最短和客户等待时间最小为目标函数, 以服务时间窗与车辆载质量为约束条件, 建立了双目标优化模型, 采用基于整数编码的多智能体进化算法求解模型, 并将计算结果与利用遗传算法求得的结果进行对比。计算结果表明: 当客户需求点的数量为13, 需求点的服务时间为5min, 车辆最大载质量为3t, 初始智能体个数为49, 最大进化代数为200次时, 经过30次计算后, 采用遗传算法的最差值为121.8min, 最优值为110.3min, 采用提出多智能体进化算法的最差值为113.6min, 最优目标值为103.6min。可见, 采用多智能体进化算法能够获得更高质量的最优解, 而且经过多次反复试验, 最终解的变化不大。     

EQ6110混合动力电动汽车再生制动控制策略研究

分析了电机再生制动对车辆制动性能的影响以及典型城市公交客车运行工况特点，提出了适于EQ6110HEV的再生制动控制策略——低制动强度时优先采用再生制动，高强度时按比例复合再生制动与摩擦制动。仿真计算表明：在各种循环工况下，EQ6110HEV采用这种再生制动控制策略均有较好的节能效果，可降低能耗10％～25％。     

国产轻型汽车排放现状与分析

本文综述了国产轻型汽车的基本情况，指出轻型汽车的排气污染物是造成汽车对大气污染的最主要来源，根据试验结果评述了国产轻型汽车的排放现状，然后进行了试验分析，最后简要介绍了影响汽车对大气污染的各种因素。     

基于故障模式、影响及危害度分析(FMECA)的地铁车辆检修工艺优化方法

提出了一种地铁车辆检修工艺优化方法。该方法采用FMECA(故障模式、影响及危害度分析),对地铁车辆运营过程中的故障数据进行分析,并根据分析结果对当前的地铁车辆检修工艺进行优化,以提高检修作业的合理性和效率。以地铁车辆高压供电系统为例,说明了该优化方法在地铁车辆高压供电系统上的实施流程;结合相关标准的规定建立了定量化的地铁车辆设备危害性风险评价矩阵,用以确定故障模式的可接受程度;通过建立可接受程度与该检修项点检修等级的对应关系,最终得到检修工艺的优化方案。     

地铁车辆计算黏着系数的取值探讨

介绍了地铁车辆计算黏着系数取值的影响因素及计算方法,并根据广州地铁二号线车辆的情况举实例进行说明。     

水下航行体空间操纵性预报研究

通过建立水下航行体空间的操纵运动数学模型,运用Visual C++设计开发水下航行体操纵运动预报系统,对水下航行体水平面、垂直面及空间典型操纵运动进行数值预报,数值模拟结果合理,能够较为全面地预报水下航行体的空间操纵运动。     

空气湿度对地铁车轮磨耗的影响及机理研究

车轮磨耗影响着地铁的运营安全和运营成本。目前,关于车轮磨耗的研究多集中于轴重、轮轨接触状态等因素的影响,考虑空气湿度影响的研究较少。通过对某城市地铁进行了长期监测,对每月的车轮平均磨耗量及空气相对湿度进行了统计分析,得出了车轮磨耗量随空气湿度增加而降低的规律;并结合已有试验数据,对这一规律进行了验证。对空气湿度影响车轮磨耗的机理进行了探讨,发现在气温未达到露点温度时,湿度主要影响轮轨接触表面的粗糙度、硬度以及氧化膜的形成;在气温达到露点温度之后,轮轨表面将会形成水膜,这将会降低轮轨间摩擦系数,降低车轮的磨耗量。