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.     

How do economies of density in container handling operations affect ships’ time and emissions in port? Evidence from Norwegian container terminals

Efficient port services are prerequisites for competitive and sustainable maritime transports. This paper makes advances in studying the determinants of the time that ships spend in port and the associated emissions to air. We estimate a production model for cargo handling based on a unique dataset containing each port of call at the largest container terminals in Norway in 2014. In turn, we use auxiliary engine emission factors to estimate particulate matter and nitrogen oxide emissions from ships at berth, to determine how the corresponding damage costs of air pollution vary with container throughput, location, and terminal investments. We find that Norwegian container terminals operate under increasing returns to density. Small ships that unload few containers are far from reaping economies of density, leading to high marginal time requirements for container handling and consequently high marginal external costs. From a Pigouvian taxation perspective, port charges should therefore be regressive in the number of containers handled. Moreover, we find that the external costs of maritime transports are severely understated when port operations are ignored. Our model allows determining the marginal productivities of port facilities. Thereby, it is instrumental in designing port charges that are diversified according to the quantity of containers handled and the service quality (i.e., the speed of handling operations). Regarding contextual factors, we find that establishing high-frequent liner services improves the ship working rate, while simultaneous calls at a terminal impede productivity. The type of container (loading/unloading; empty/laden) also appears to influence the duration of ship working.     

基于内燃动车组的混合动力匹配设计研究

针对内燃动力轨道交通在实际应用中存在的不足以及当前主要新能源技术发展现状,文章结合某内燃动车组的总体参数和配置情况,将内燃机组匹配蓄电池的混合动力方案与纯内燃机组方案进行对比分析,并对混合动力选型和配置进行了计算和分析。结果表明,混合动力匹配方案在降低整车能耗和节省燃油消耗等方面有着显著的优势。     

复杂载荷下裂纹SIF计算的子模型技术

对船舶与海洋工程结构物进行疲劳评估,首先要通过子模型技术解决复杂应力场中裂纹应力强度因子(SIF)的计算问题。针对子模型技术实现过程繁杂且效率较低的问题,分别提出"逐周分层法","转换矩阵法"及"映射划分法"并基于VBA及APDL语言编写插件MPCarrangerV1.0,FEMcoortransferV1.0及CrackmapperV1.0,解决从Patran整体有限元模型到Ansys子模型时壳体单元间MPC创建低效、不同坐标系间节点位置转换困难及裂纹自由划分的局限性问题。基于DNV及ABS相关规范,以某B型LNG燃料舱的疲劳热点为例,对改进的子模型技术进行验证,结果表明经改进的子模型技术可成功施加合理边界条件并实现SIF的求解,可为子模型技术快速实现复杂载荷下裂纹SIF的准确计算提供参考。     

基于生长曲线函数的货车运营环节碳达峰研究

货车是我国大气环境污染的重要来源之一，也是影响我国碳达峰总体目标实现的重要因 素。本文从货车运营环节入手，在运用生长曲线函数对货车保有量进行预测的基础上，对不同类 型货车的保有量和单车碳排放变化进行研究，并从货车节能技术发展、新能源货车推广和应用进 程两方面入手，分3种情景对货车运营环节中产生的碳排放总量趋势进行预判，推演货车运营环 节的碳达峰时间。研究结果表明，只有同时加快货车节能技术发展以及新能源货车推广和应用 进程，货车运营环节中产生的碳排放总量规模才能得到有效抑制并逐渐减少。若到 2030 年货 车单车燃料消耗水平较 2019 年降低 20%以上，新能源货车在货车整体保有量中的占比达到 20%，到 2060 年货车单车燃料消耗水平较 2019 年降低 50%，新能源货车占比达到 50%，则货车 运营环节碳达峰时间将缩短至2030年左右实现，2030年后货车运营环节产生的碳排放总量规 模将逐渐减少。     

Are technology myths stalling aviation climate policy?

Emissions from aviation will continue to increase in the future, in contradiction of global climate policy objectives. Yet, airlines and airline organisations suggest that aviation will become climatically sustainable. This paper investigates this paradox by reviewing fuel-efficiency gains since the 1960s in comparison to aviation growth, and by linking these results to technology discourses, based on a two-tiered approach tracing technology-focused discourses over 20 years (1994–2013). Findings indicate that a wide range of solutions to growing emissions from aviation have been presented by industry, hyped in global media, and subsequently vanished to be replaced by new technology discourses. Redundant discourses often linger in the public domain, where they continue to be associated with industry aspirations of ‘sustainable aviation’ and ‘zero-emission flight’. The paper highlights and discusses a number of technology discourses that constitute ‘technology myths’, and the role these ‘myths’ may be playing in the enduring but flawed promise of sustainable aviation. We conclude that technology myths require policy-makers to interpret and take into account technical uncertainty, which may result in inaction that continues to delay much needed progress in climate policy for aviation.     

Fueling for contingencies: The hidden cost of unpredictability in the air transportation system

The aviation community is increasing its attention on the concept of predictability when conducting aviation service quality assessments. Reduced fuel consumption and the related cost is one of the various benefits that could be achieved through improved flight predictability. A lack of predictability may cause airline dispatchers to load more fuel onto aircraft before they depart; the flights would then in turn consume extra fuel just to carry excess fuel loaded. In this study, we employ a large dataset with flight-level fuel loading and consumption information from a major US airline. With these data, we estimate the relationship between the amount of loaded fuel and flight predictability performance using a statistical model. The impact of loaded fuel is translated into fuel consumption and, ultimately, fuel cost and environmental impact for US domestic operations. We find that a one-minute increase in the standard deviation of airborne time leads to a 0.88 min increase in loaded contingency fuel and 1.66 min in loaded contingency and alternate fuel. If there were no unpredictability in the aviation system, captured in our model by eliminating standard deviation in flight time, the reduction in the loaded fuel would between 6.12 and 11.28 min per flight. Given a range of fuel prices, this ultimately would translate into cost savings for US domestic airlines on the order of $120–$452 million per year.     

Can we reliably assess climate mitigation options for air traffic scenarios despite large uncertainties in atmospheric processes?

Air traffic has an increasing influence on climate; therefore identifying mitigation options to reduce the climate impact of aviation becomes more and more important. Aviation influences climate through several climate agents, which show different dependencies on the magnitude and location of emission and the spatial and temporal impacts. Even counteracting effects can occur. Therefore, it is important to analyse all effects with high accuracy to identify mitigation potentials. However, the uncertainties in calculating the climate impact of aviation are partly large (up to a factor of about 2). In this study, we present a methodology, based on a Monte Carlo simulation of an updated non-linear climate-chemistry response model AirClim, to integrate above mentioned uncertainties in the climate assessment of mitigation options. Since mitigation options often represent small changes in emissions, we concentrate on a more generalised approach and use exemplarily different normalised global air traffic inventories to test the methodology. These inventories are identical in total emissions but differ in the spatial emission distribution. We show that using the Monte Carlo simulation and analysing relative differences between scenarios lead to a reliable assessment of mitigation potentials. In a use case we show that the presented methodology can be used to analyse even small differences between scenarios with mean flight altitude variations.     

Asymmetry in transport fuel demand: Evidence from household level data

Models for gasoline demand for transportation activities generally assume that demand is perfectly reversible with respect to gasoline price (and income). The small literature which relaxes the reversibility assumption in gasoline demand argues technological fixation leads to this asymmetry and utilizes aggregate time-series model to find evidence in favour of asymmetry. In this research it is suggested that there could also be behavioural factors behind this asymmetric response, possibly due to the loss aversion nature of human beings as described in the prospect theory. For the first time, household level data was used to understand asymmetry in gasoline demand in response to changes in gasoline price and income. There was statistical evidence that gasoline price and income both can induce asymmetric changes in gasoline demand among households. Specifically, elasticity with respect to rising prices and falling income is larger than the elasticity with respect to falling prices and rising income respectively, which is consistent with loss aversion in gasoline purchase behaviour. There was also some evidence of heterogeneity in the asymmetric responses between urban and rural households. The results have implications for transport-related energy tax policies or subsidies, while the method can be applied directly for non-energy goods as well.     

Wildlife strikes to civil helicopters in the US, 1990–2011

This paper examines wildlife strikes with civil helicopters within the US. Month and time of day, location, and other factors influenced the frequency of wildlife strikes with civil helicopters. Wildlife strikes occurred most frequently when the aircraft were traveling en route or engaged in terrain flight. Birds accounted for over 97% of the wildlife strikes where the animal was identified.