Real-world fuel consumption of light-duty passenger vehicles using on-board diagnostic (OBD) systems

• Fuel consumption (FC) from LDPVs is measured using on-board diagnostic method (OBD). • The FC of the OBD is 7.1% lower than that of the carbon balance results. • The discrepancy between the approved FC and real-world FC is 13%±18%. • There is a strong relationship (R²=0.984) between the average speed and relative FC. An increasing discrepancy between real-world and type-approval fuel consumption for light-duty passenger vehicles (LDPVs) has been reported by several studies. Normally, real-world fuel consumption is measured primarily by a portable emission measurement system. The on-board diagnostic (OBD) approach, which is flexible and offers high-resolution data collection, is a promising fuel consumption monitoring method. Three LDPVs were tested with a laboratory dynamometer based on a type-approval cycle, the New European Driving Cycle (NEDC). Fuel consumption was measured by the OBD and constant-volume sampling system (CVS, a regulatory method) to verify the accuracy of the OBD values. The results of the OBD method and the regulatory carbon balance method exhibited a strong linear correlation (e.g., R² = 0.906-0.977). Compared with the carbon balance results, the fuel consumption results using the OBD were 7.1%±4.3% lower on average. Furthermore, the real-world fuel consumption of six LDPVs was tested in Beijing using the OBD. The results showed that the normalized NEDC real-world fuel consumption of the tested vehicles was 13%±17% higher than the type-approval-based fuel consumption. Because the OBD values are lower than the actual fuel consumption, using a carbon balance method may result in a larger discrepancy between real-word and type-approval fuel consumption. By means of the operating mode binning and micro trip methods, a strong relationship (R² = 0.984) was established between the average speed and relative fuel consumption. For congested roads (average vehicle speed less than 25 km/h), the fuel consumption of LDPVs is highly sensitive to changes in average speed.     

Two-stage vertex analysis method for planning electric power systems with greenhouse gas abatement consideration

This study presents a two-stage vertex analysis (TSVA) method for the planning of electric power systems (EPS) under uncertainty. TSVA has advantages in comparison to other optimization techniques. Firstly, TSVA can incorporate greenhouse gas (GHG) abatement policies directly into its optimization process, and, secondly, it can readily integrate inherent system uncertainties expressed as fuzzy sets and probability distributions directly into its modeling formulation and solution procedure. The TSVA method is applied to a case study of planning EPS and it is demonstrated how the TSVA efficiently identify optimal electricity-generation schemes that could help to minimize system cost under different GHG-abatement considerations. Different combinative considerations on the uncertain inputs lead to varied system costs and GHG emissions. Results reveal that the total electricity supply will rise up along with the time period due to the increasing demand and, at the same time, more non-fossil fuels should be used to satisfy the increasing requirement for GHG mitigation. Moreover, uncertainties in connection with complexities in terms of information quality (e.g., capacity, efficiency, and demand) result in changed electricity-generation patterns, GHG-abatement amounts, as well as system costs. Minimax regret (MMR) analysis technique is employed to identify desired alternative that reflects compromises between system cost and system-failure risk.     

重污染天气应对工作中机动车污染减排措施研究

伴随着国民经济的高速发展,越来越多的人拥有了私家车,随之而来的天气污染问题也越来越严重。基于此,本文针对重污染天气应对工作中机动车污染减排措施进行研究。通过加强机动车尾气排放管理、发展太阳能汽车和推动公交出行这三个措施,带动广大市民参与到机动车污染减排的工作中。     

State-level seat belt use in the United States, 2011–2016: Comparison of self-reported with observed use and use by fatally injured occupants

Introduction: Despite 49 states and the District of Columbia having seat belt laws that permit either primary or secondary enforcement, nearly half of persons who die in passenger vehicle crashes in the United States are unbelted. Monitoring seat belt use is important for measuring the effectiveness of strategies to increase belt use. Objective: Document self-reported seat belt use by state seat belt enforcement type and compare 2016 self-reported belt use with observed use and use among passenger vehicle occupant (PVO) fatalities. Methods: We analyzed the Behavioral Risk Factor Surveillance System (BRFSS) self-reported seat belt use data during 2011–2016. The Pearson correlation coefficient (r) was used to compare the 2016 BRFSS state estimates with observed seat belt use from state-based surveys and with unrestrained PVO fatalities from the Fatality Analysis Reporting System. Results: During 2011–2016, national self-reported seat belt use ranged from 86–88%. In 2016, national self-reported use (87%) lagged observed use (90%) by 3 percentage points. By state, the 2016 self-reported use ranged from 64% in South Dakota to 93% in California, Hawaii, and Oregon. Seat belt use averaged 7 percentage points higher in primary enforcement states (89%) than in secondary states (82%). Self-reported state estimates were strongly positively correlated with state observational estimates (r = 0.80) and strongly negatively correlated with the proportion of unrestrained PVO fatalities (r = −0.77). Conclusion: National self-reported seat belt use remained essentially stable during 2011–2016 at around 87%, but large variations existed across states. Practical Applications: If seat belt use in secondary enforcement states matched use in primary enforcement states for 2016, an additional 3.98 million adults would have been belted. Renewed attention to increasing seat belt use will be needed to reduce motor-vehicle fatalities. Self-reported and observational seat belt data complement one another and can aid in designing targeted and multifaceted interventions.     

Simulating the relative influence of tire,vehicle and driver factors on forward collision accident rates

Introduction: There is currently a strong focus within the automotive industry centered on traffic safety, with topics such as distracted driving, accident avoidance technologies, and autonomous vehicles. These papers tend to focus on the possible improvements from a single factor. However, there are many factors that are present in each accident, and it is important to understand the influence of each factor on the relative accident risk in order to identify the most effective approaches for improving driver safety. Rear-end accidents tend to be the most common accident type with approximately 1.8 M cases, or 31% of all accidents, in 2012, according to NHTSA. Of the rear-end accident scenarios, approximately 18–23% occur on wet surfaces. Method: A Monte Carlo Forward Collision Simulation models the conditions of a wet rear-end accident and estimates the relative impact of various vehicle collision parameters. The model takes distributions of these parameters as inputs, and outputs a risk of collision relative to a known reference case. The parameters that can be studied include: tire grip level, road grip level, vehicle velocity, following distances, and the presence of vehicle technologies (ABS, FCW & AEB). Distributions of some of these parameters have been improved thanks to Naturalistic Driving Study data from SHRP2. Results: This study shows that these vehicle systems have a large impact on safety and can change the amount of influence attributed to other parameters such as tire grip levels. As the use of automated vehicle systems expands, so will the influence of tire grip performance levels on collision risks. Practical Applications: It is more important than ever for consumers and auto manufacturers to consider tire performance levels. Therefore, the tire industry should continue to focus on wet grip as a key performance related to safety and should strive to continue to improve tire performance.     

浅析成都市机动车保有量的增加对大气污染物中二氧化氮浓度的影响

本文利用成都市城区近4年的环境空气二氧化氮浓度资料,结合成都市机动车保有量,分析了成都市机动车保有量的增加对空气中二氧化氮浓度的影响。分析表明,成都市机动车保有量的增加对空气中二氧化氮浓度有明显影响,尤以交通干道附近最为显著,二氧化氮浓度较非交通干线分别偏高12.0%～47.8%,成都市采取的高污染车限行等措施对二氧化氮浓度的控制起了重要作用。     

基于热像技术的电力变压器热故障的诊断方法

提出了一种基于变压器表面的红外热像对变压器热故障进行在线诊断的方法.根据变压器的表面温度、结构参数、物性参数和环境温度通过建立其传热模型,对变压器内部热故障温度进行反演计算,并与DGA诊断结果进行比较,证明了该方法的可行性,实现了变压器热故障的非接触、在线诊断.     

考虑区域特点和车型差异的氢燃料电池汽车全生命周期减碳预测分析

发展氢燃料电池汽车是我国实现“双碳”战略的重要路径之一,目前我国多个区域正在推广应用包括乘用车、客车以及重卡在内的氢燃料电池汽车,如何量化研究未来不同车型和不同区域的氢燃料电池汽车减碳潜力成为如今的研究热点之一.基于全生命周期的评价方法,考虑了未来的汽车燃油经济性、电力碳排放因子、氢能碳排放因子和氢燃料电池汽车推广规模及制氢方式的区域差异,量化评价了不同类型的氢燃料电池汽车（FCV）、传统燃油汽车（ICEV）和纯电动汽车（BEV）的全生命周期碳排放量（以CO₂当量计）,对比分析了氢燃料电池汽车在不同时间及不同区域的减碳潜力,并对百公里氢耗量进行了不确定性分析.结果发现,到2025年氢燃料电池客车的全生命周期碳排放比传统燃油客车降低36.0%,而氢燃料电池重卡相较于传统燃油重卡并没有减碳效益.随着未来我国氢能来源结构的不断优化,到2035年氢燃料电池重卡的全生命周期碳排放比传统燃油重卡降低36.5%,相较于乘用车和客车两种车型,其减碳效益是最明显的.以2035年京津冀示范群为例,随着百公里氢耗量降低20%,FCV乘用车、客车和重卡的减碳规模分别增加了7.29%、9.93%和19.57%.因此建议氢燃料电池汽车推广应短期以客车为主,长期以重卡为主,乘用车推广作为补充.分区域和分阶段推广氢燃料电池汽车更有助于推进我国汽车领域的低碳化进程.     

饮用水消毒副产物处理技术及其进展

论述和比较了饮用水中消毒副产物的各种控制技术,提出电场处理因其高效、成本低廉和不带二次污染等特点,是未来饮用水消毒副产物极有前途和实用的处理技术,并结合目前研究进展指出该技术的不足和亟待完善的方向。     

轻型汽油车VOCs排放特征及其大气反应活性

采用车载式尾气测量系统对国Ⅱ、国Ⅲ、国Ⅳ轻型汽油车在实际道路排放的尾气进行样品采集,并采用GC-MS、GC-FID对尾气中烷烃、烯烃、苯系物进行测试分析.结果表明轻型汽油车的VOCs排放因子随排放标准的提高显著降低,国Ⅱ、国Ⅲ、国Ⅳ3种车型的排放因子分别为49.62、21.65、6.72mg/km.苯系物占测定VOCs组分的比例最高,占到总VOCs的47.43%~60.52%.由排放的VOCs估算获得不同标准车型的臭氧生成潜势及二次有机气溶胶生成潜势分别为24.64~234.14mg/km和13.24~125.32mg/km.在对国Ⅲ车型进行的不同速度的实验结果显示,轻型汽油车尾气VOCs排放因子及相应的臭氧生成潜势和二次有机气溶胶生成潜势均随实验车速的升高而降低.