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.     

城市轨道交通车辆产品标准体系探索

分析了国内外城市轨道交通车辆产品标准体系的现状,重点介绍了城市轨道交通车辆产品标准体系的总体要求、结构搭建、产品标准的制修订,以及标准的实施和改进措施。     

长大货物车适应THDS轴温探测技术的研究

针对不适应THDS轴温探测系统的长大货物车,分析论证了现车轴箱改进方案适应THDS轴温探测的不足,并提出了轴箱优化方案。经计算、试验和装车验证表明,新轴箱可行、有效、安全,满足THDS轴温探测要求,保证了长大货物车运输安全。     

酚醛树脂复合材料在轨道交通车辆上的应用

介绍了酚醛树脂复合材料试制的主要原材料、主要设备及仪器、试制流程及测试标准。详细阐述了酚醛树脂复合材料的氧指数、燃烧性能、机械性能、低密度等性能。分析了热压时间、热压温度及生产工艺对酚醛树脂复合材料性能的影响。试验证明,酚醛树脂产品的性能较为优良:热压时间控制在1.1~1.4 h时,复合材料的表面性能良好;热压温度为80~100℃时,可以得到表面质量细腻均匀的复合材料;适当调整生产工艺,可以提高酚醛树脂的产品质量、降低生产成本。     

25t轴重荷载作用下隧底脱空区域受力特性分析

为了研究在役铁路隧道在通车之后隧底脱空病害的问题,采用有限元理论,建立隧道脱空区域在围岩压力与25 t轴重列车动载作用下的数值计算模型,主要研究80 cm与40 cm脱空宽度分别距隧道中心线0,80 cm与160 cm时脱空区域的受力特性。结果表明:在围岩压力下,脱空区域中线上壁和外侧顶角混凝土中产生拉应力及内侧顶角中产生压应力,其中压应力对脱空的宽度更为敏感;同时施加列车动载作用时,脱空区域上壁出现了竖向动应力与横向拉应力,得到了脱空区域力学指标的最大响应值及其出现的具体位置,宽度的增加对脱空区上壁横向拉应力更为显著,上壁横向拉应力增幅超过200%,竖向动应力增幅达50%。因此,隧底脱空区周围应力分布复杂,拉应力与压应力在脱空区域同时存在,应力突变严重,对脱空现象应及时组织处理。     

爆炸硬化高锰钢组合辙叉水平裂纹伤损分析

爆炸硬化高锰钢组合辙叉是近几年针对重载铁路使用条件开发的一种新型固定型辙叉产品,在批量上道使用后,水平裂纹伤损发生频次逐渐增加,引起广泛的关注。对水平裂纹伤损产生的原因进行分析,并提出多项改进措施。通过改进措施的实施,在朔黄线取得了较好的改进效果。实际结果表明,通过不断改进工艺、提高质量、加强维护,爆炸硬化高锰钢组合辙叉能够实现通过总重3亿t以上的目标。     

STX4型驮背多功能运输车车体制造工艺

介绍了STX4型驮背多功能运输车的主要结构特点,分析了车体制造过程中的控制要点和难点,同时对制造难点提出了应对措施,有效保证了STX4型驮背多功能运输车的制造质量。     

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.     

Fast-charging station choice behavior among battery electric vehicle users

This study explores how battery electric vehicle users choose where to fast-charge their vehicles from a set of charging stations, as well as the distance by which they are generally willing to detour for fast-charging. The focus is on fast-charging events during trips that include just one fast-charge between origin and destination in Kanagawa Prefecture, Japan. Mixed logit models with and without a threshold effect for detour distance are applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Findings from the mixed logit model with threshold show that private users are generally willing to detour up to about 1750 m on working days and 750 m on non-working days, while the distance is 500 m for commercial users on both working and non-working days. Users in general prefer to charge at stations requiring a shorter detour and use chargers located at gas stations, and are significantly affected by the remaining charge. Commercial users prefer to charge at stations encountered earlier along their paths, while only private users traveling on working days show such preference and they turn to prefer the stations encountered later when choosing a station in peak hours. Only private users traveling on working days show a strong preference for free charging. Commercial users tend to pay for charging at a station within 500 m detour distance. The fast charging station choice behavior is heterogeneous among users. These findings provide a basis for early planning of a public fast charging infrastructure.     

Optimal design of electric vehicle public charging system in an urban network for Greenhouse Gas Emission and cost minimization

In this paper, we address the optimization problem of allocation of Electric Vehicle (EV) public fast charging stations over an urban grid network. The objective is to minimize Greenhouse Gas Emissions (GHG) under multiple constraints including a limited agency budget, accessibility of charging stations in every possible charging request and charging demands during peak hours. Additionally, we address bi-criteria problems to consider user costs as the second objective. A convex parsimonious model that depends on relatively few assumptions and input parameters is proposed and it is shown to be useful for obtaining conceptual insights for high-level planning. In a parametric study using a hypothetical urban network model generated based on realistic parameters, we show that GHG emissions decrease with agency budget, and that the reductions vary depending on multiple factors related to EV market and EV technologies. The optimal solutions found from the bi-criteria problems are shown to be close to the solution minimizing GHG emissions only, meaning that the emission minimizing policy can also minimize user costs.