Powertrain technologies and their impact on greenhouse gas emissions in key car markets

The role alternative car technologies may play in effectively tackling the problem of climate change is still highly uncertain. This paper aims at investigating possible impacts of car powertrain technologies on future energy demand and its corresponding greenhouse gas emissions until 2030. A system dynamics model covering nine car technologies in China, France, Germany, India, Japan and the United States was applied, with a focus on electric cars. Four main scenarios are constructed and sensitivity analysis undertaken. Greenhouse gas emissions from cars in the six countries are simulated to reach up to 2.6 gigatonnes in 2030 (a 13–32% increase between 2020 and 2030, depending on the scenario). The main conclusion from model-based policy analysis is that electric cars may have a positive contribution to emissions mitigation in the passenger road transport system. However, greenhouse gas emissions from cars arising from the combined effect of car manufacturing and scrappage and electricity generation processes are expected to grow more dramatically. As a result, actions that support both low-emission (re-)manufacturing and clean electricity generation are needed. These results complement accurate but static life cycle assessments and open the discussion for dynamic model assumptions.     

基于AHP-DEMATEL的高铁枢纽换乘效率关键因素研究

为了找出影响高铁枢纽换乘效率关键因素,科学合理高铁枢纽布局,提出一种基于AHPDEMATEL组合评价方法。以高铁枢纽为评价对象,通过构建具有层次结构的评价指标体系,采用层次分析法(AHP)确定指标初始权重,决策与试验评价实验室法(DEMATEL)求得各评价指标影响度、被影响度、中心度,从而确定各指标综合影响度。研究结果表明:其他交通衔接性、通道便捷性、设施供给度、枢纽组织有序度、停车场供需比、平均换乘时间、平均换乘距离、单位时间换乘量、绕行系数是影响高铁枢纽换乘效率的关键因素。     

Impact of government incentives on the market penetration of electric vehicles in Australia

Incentives to buy and use electric vehicles (EVs) may influence individuals’ decisions to do so. To examine these impacts, a latent class discrete choice model is developed to analyse consumer preferences related to EV attributes and related government incentives. Data was collected from a stated preference survey of 1,076 residents of New South Wales (NSW), Australia. According to the results, the proposed latent constructs classify respondents into five segments. The segments are then used to distinguish respondent behaviours regarding EV attributes and related government incentives. The results show that rebate on the upfront cost of an EV is the most preferred one-off financial incentive, because EVs are expected to be expensive, especially in Australia which has a very small EV market at present. Furthermore, rebates on energy bills and parking fees are also well-received, as these things are expensive in Sydney, Australia. Thus, operational incentives for discounts on energy bills and parking fees may facilitate the success of EVs in NSW.     

基于BIM的综合交通枢纽管线综合设计研究——以京张高铁清河站设计为例

随着时代的发展,土地集约利用已成趋势,综合性交通枢纽越来越多。在设计中,交通枢纽建筑具有功能多样、专业繁多、接口复杂的特点,为保证其不同空间效果和功能使用要求,综合管线设计越来越重要。从综合管线设计原则出发,结合京张高铁清河站管线综合设计实例,阐述综合管线一体化设计的思路和重难点。并以BIM技术在碰撞检查、验证复杂空间、解决净高、优化设计、三维交底5个方面的优势,分析BIM辅助设计的必要性。最终说明综合管线排布可以保证建筑空间的合理利用并从工序上指导施工:通过BIM技术深化设计,对提高设计准确性,提升管线施工效率和安装质量有重要的意义,为综合交通枢纽管线综合设计提供借鉴。     

Sustainable urban transit network design

Sustainability is a requirement for modern public transportation networks, as these are expected to play a critical role in environment-friendly transportation systems. This paper focuses on developing an efficient model for solving a sustainable oriented variant of the Transit Route Network Design Problem. The model incorporates sustainable design objectives, considers emission-free (electric) vehicles and introduces a direct route design approach with route structure and directness control. An application in a real world case, highlights the performance and benefits of the proposed model.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.     

郑州南枢纽分步开通方案研究

作为郑万铁路工程的一部分,郑州南站枢纽受工程进度及开通时间影响,无法与郑万高铁正线工程同步开通,需考虑采用分步过渡开通方案。对郑州南站枢纽分步过渡开通方案进行深入研究,提出合理建议。     

Assessing the impact of cornering losses on the energy consumption of electric city buses

In view of the increasing electrification of public city transport, an accurate energy consumption prediction for Battery Electric Buses (BEBs) is essential. Conventional prediction algorithms do not consider energy losses that occur during turning of the vehicle. This is especially relevant for electric city buses, which have a limited battery capacity and often drive curvy routes.In this paper, the additional energy consumption during steering of a BEB is modeled, measured, and assessed. A nonlinear steady-state cornering model is developed to establish the additional energy losses during cornering. The model includes large steer angles, load transfer, and a Magic Formula tire model. Model results show that both cornering resistance and tire scrub of the rear tires cause additional energy losses during cornering, depending on the corner radius and vehicle velocity.The energy consumption model is validated with full scale vehicle tests and shows an average deviation of 0.8 kW compared to the measurements. Analysis of recorded real-world bus routes reveals that on average these effects constitute 3.1% of the total powertrain energy. The effect is even more significant for routes crossing city centers, reaching values up to 5.8%. In these cases, cornering losses can be significant and should not be neglected in an accurate energy consumption prediction.     

From e-bike to car: A study on factors influencing motorization of e-bike users across China

Household car ownership has risen dramatically in China over the past decade. At the same time a disruptive transportation technology emerged, the electric bike (e-bike). Most studies investigating motorization in China focus on macro-level economic indicators like GDP, with few focusing on household, city-level, environmental, or geographic indicators, and none in the context of high e-bike ownership. This study examines household vehicle purchase decisions across 59 cities in China with broad geographic, environmental, and socio-economic characteristics. We focus on a subset of households who own e-bikes and rely on a telephone survey from an industry customer database. From these responses, we estimate two three-level hierarchical choice models to assess attributes that contribute to (1) recent car purchases and (2) the intention to buy a car in the near future. The results show that the models are dominated by household characteristics including household income, household size, household vehicle ownership, number of licensed drivers and duration of car ownership. Some geographic, environmental and socio-economic factors have significant influences on car purchase decisions. Only two city-level transportation variable have an effect – higher taxi density and higher bus density reducing car purchase. Cold weather, population density gross domestic product per capita positively influence car purchase, while urbanization rate reduces car purchase. Because of supply heterogeneity in the data set, described by publicly available urban transportation data, this is the first study that can include geographic and urban infrastructure differences that influence purchase choice and suggests potential region-specific policy approaches to managing car purchase may be necessary.     

Factors and scenario analysis of transport carbon dioxide emissions in rapidly-developing cities

To identify key factors of transport CO₂ emissions and determine effective policies for emission reductions in fast-growing cities, this study establishes transport CO₂ emission models, quantifying the influences of polycentricity and satellite cities and re-examining the effects of per capita GDP and metro service. Based on the model results, we forecast future residents’ urban transport CO₂ emissions under several scenarios of different urban and transport policies and new energy technologies. We find nonlinear quadratic growth relationship between commuting CO₂ emissions and per capita GDP, and the elasticities of household and individual commuting CO₂ emission to per capita GDP are 1.90% and 1.45%, respectively. Developing job-housing balanced satellite cities and self-contained polycentric city can greatly decrease emissions from high emitters and can contribute to about 51–82% of the emission reductions by 2050 compared with the scenario of business as usual (BAU). Promotion of electric vehicles, electric public buses, metros, and improvement of traditional energy efficiency contributes to about 48–57% of the emission reductions by 2050 compared with the BAU. When these policies and technologies are combined, about 90% of the emissions could be reduced by 2050 compared with the BAU, and the emissions will be about 1.2–4.9 times of the present. The findings suggest that fostering polycentric urban form and job-housing balanced satellite cities is the key step for future transport CO₂ emission reductions. Metro network promotion, energy efficiency improvement, and new energy type applications can also be effective in emission reductions.