On-road Vehicles Research Articles

Emissions Control Scenarios for Transport in Greater Cairo.

Air pollution is a major cause of premature death in Greater Cairo, but studies on emission control are limited. We used local and international data to predict the impact of transport emission control measures on sector parameters including congestion. The International Vehicle Emission model accordingly estimated quantities of criteria, toxic and global warming emissions produced by on-road vehicles. Emissions were estimated for 2019 base case (2019-BC) and projected for 2030 under the ‘do nothing’ scenario (2030-DNS) and five scenarios: fuel subsidy removal (2030-FSR), road expansions (2030-RE), public transport improvements (2030-PTI), inspection and maintenance (I/M) programs (2030-I/MP), and fuel enhancements (2030-FE). The 2030-FSR would reduce emissions by 11.2% versus 2030-DNS. The 2030-RE resulted in an average increase of 37% in emissions compared with 2030-DNS since it induces more traffic. The 2030-PTI provides alternatives to car travel; hence, cars result in an average drop of 32.8% for all emission types compared with 2030-DNS. The 2030-I/MP exhibited reductions in PM10 and toxic pollutants, of 35–54.8% compared with 2030-DNS. The 2030-FE reduced SOx, benzene and N2O emissions by 91.8%, 81% and 39.1%, respectively, compared with 2030-DNS. The 2030-I/MP is most effective in reducing health damaging pollutants while 2030-PTI positively impacts commuters’ lifestyle.

Life Cycle Assessment of Autonomous Electric Field Tractors in Swedish Agriculture

There is an increased interest for battery electric vehicles in multiple sectors, including agriculture. The potential for lowered environmental impact is one of the key factors, but there exists a knowledge gap between the environmental impact of on-road vehicles and agricultural work machinery. In this study, a life cycle assessment was performed on two smaller, self-driving battery electric tractors, and the results were compared to those of a conventional tractor for eleven midpoint characterisation factors, three damage categories and one weighted single score. The results showed that compared to the conventional tractor, the battery electric tractor had a higher impact in all categories during the production phase, with battery production being a majority contributor. However, over the entire life cycle, it had a lower impact in the weighted single score (−72%) and all three damage categories; human health (−74%), ecosystem impact (−47%) and resource scarcity (−67%). The global warming potential over the life cycle of the battery electric tractor was 102 kg CO2eq.ha−1 y−1 compared to 293 kg CO2eq.ha−1 y−1 for the conventional system. For the global warming potential category, the use phase was the most influential and the fuel used was the single most important factor.

A Model for the Estimation of the Residual Driving Range of Battery Electric Vehicles Including Battery Ageing, Thermal Effects and Auxiliaries

Sustainable mobility has recently become a priority of research for on-road vehicles. Shifting towards vehicle electrification is one of the most promising solutions concerning the reduction in pollutant emissions and greenhouse gases, especially for urban areas. Nevertheless, battery electric vehicles might carry substantial limitations compared with other technologies. Specifically, the electric range could be highly affected by the ageing process, non-optimal thermal management of the battery and cabin conditioning. In this paper, a model for the estimation of the residual range of electric vehicles is proposed accounting for the influence of battery state of health, battery pack temperature, power consumption of the main vehicle auxiliaries, and battery pre-heating on the residual driving range. The results of the model application to an L7 battery electric vehicle highlighted that the electric range can be highly affected by several factors related to real-world driving conditions and can consistently differ from nominal values.

Particulate emissions of real-world light-duty gasoline vehicle fleet in Iran

Fine particulate matter cause profound adverse health effects in Iran. Road traffic is one of the main sources of particulate matter (PM) in urban areas, and has a large contribution in PM2.5 and organic carbon concentration, in Tehran, Iran. The composition of fine PM vehicle emission is poorly known, so this paper aims to determine the mixed fleet source profile by using the analysed data from the two internal stations and the emission factor for PM light-duty vehicles emission. Tunnels are ideal media for extraction vehicle source profile and emission factor, due to vehicles are the only source of pollutant in the urban tunnels. In this study, PM samples were collected simultaneously in two road tunnel stations and at a background site in Niyayesh tunnel in Tehran, Iran. The tunnel samples show a large contribution for some elements and ions, such as Fe (0.23 μg μg−1 OC), Al (0.02 μg μg−1 OC), Ca (0.055 μg μg−1 OC), SO4 (0.047 μg μg−1 OC), Docosane (0.0017 μg μg−1 OC), Triacontane (0.016 μg μg−1 OC), Anthracenedione (0.0003 μg μg−1 OC) and Benzo-perylene (0.0002 μg μg−1 OC). In overall, on-road gasoline vehicle fleets source profile extracted in this study is similar to composite profiles derived from roadside tunnel measurment performed in other countries during the last decades. The PM2.5 emission factor for Tehran's light-duty vehicle fleet has been extracted 16.23 mg km−1. vehicle−1and 0.09 g kg−1. The profile would be used for Chemical Mass Balance Model studies for Iran and other countries with a similar road traffic fleet mix. Also, it would be very suitable for use in emission inventories improvement. The results of this study can be used for choosing the best management strategies and provide comperhensive insight to fine PM traffic emission in Tehran.

Real-Time Trajectory Planning for On-road Autonomous Tractor-Trailer Vehicles

Real-Time Trajectory Planning for On-road Autonomous Tractor-Trailer Vehicles

Development in energy storage system for electric transportation: A comprehensive review

The serious growing concern of climate change has pushed the adoption of a sustainable transportation system. In this regard, electric vehicles are the utmost feasible solution. However, the commercialization of on-road electric vehicles faces multiple challenges such as an adequate number of charging stations availability, charging time and range anxiety. To overcome the issues of charging time and range anxiety, the energy storage system plays a vital role. Thus, in this paper, the various technological advancement of energy storage system for electric vehicle application has been covered which includes the support for the superiority of the Li-ion batteries in terms of various parameters. The various aspect such as expected futurist development in EV battery technology, capacity demand, battery sizing and life cycle of Li-ion batteries are well explained. Further, the details of the Battery management system with working and operation have been explained under the cell balancing, consistency, charging/discharging control, state of charge, state of health, and improved life span. Next in the drift, the advancement in the battery thermal management system is covered. Moreover, the manuscript has covered the detailed significance of the alternative energy storage systems followed by the comparative analysis which reveals their challenges, application and limitations. In last the role of the energy storage system for sustainable development is explained.

Multipurpose Longitudinal Distance-Based Driver for On-Road and Off-Road Vehicles

Multipurpose Longitudinal Distance-Based Driver for On-Road and Off-Road Vehicles

Identification of on-road vehicle CO2 emission pattern in China: A study based on a high-resolution emission inventory

As the country with the largest anthropogenic CO2 emission, China is greatly influential on fighting with climate change. However, CO2 emitted from on-road vehicles in China is a barrier to its carbon neutral due to the inevitable increase trend of vehicle quantity. Thus, to meet the requirement of refined policy-making for vehicular CO2 abatement, we calculated the CO2 emissions from 37 vehicular types and 3 fuel categories across 339 cities in China through the bottom-up method, and developed a national vehicular CO2 emission inventory in a high spatial resolution (1 km × 1 km). Additionally, machine learnings were conducted to the inventory for the emission pattern identification. It was found that the total vehicular CO2 emission in 2019 was 1090 million tons, and 77.1% of CO2 was emitted by vehicles of light-duty gasoline vehicle (LDGV) and heavy-duty diesel truck (HDDT). In addition, for the grids accompanying CO2 emissions, 75% of vehicular CO2 emissions were contributed by 15% of grids (hot grids). Furthermore, results of machine learning showed that LDGVs mainly distributed in economically advanced regions of which vehicular structures were relatively simple, while HDDTs were widely applied on the national scale. Based on the results above, two measures were proposed: (1) Electric cars have to be strongly promoted in hot grids for the LDGV replacement. (2) Long-distance freight tool replacements are urgently required in the national wide. Our study provided a studying base for further investigations on decarbonization and a new insight of China's vehicular CO2 emission controls.

Long-term trends in nitrogen oxides concentrations and on-road vehicle emission factors in Copenhagen, London and Stockholm

Road transport is the main anthropogenic source of NOx in Europe, affecting human health and ecosystems. Thus, mitigation policies have been implemented to reduce on-road vehicle emissions, particularly through the Euro standard limits. To evaluate the effectiveness of these policies, we calculated NO2 and NOx concentration trends using air quality and meteorological measurements conducted in three European cities over 26 years. These data were also employed to estimate the trends in NOx emission factors (EFNOx, based on inverse dispersion modeling) and NO2:NOx emission ratios for the vehicle fleets under real-world driving conditions. In the period 1998–2017, Copenhagen and Stockholm showed large reductions in both the urban background NOx concentrations (−2.1 and −2.6% yr−1, respectively) and EFNOx at curbside sites (68 and 43%, respectively), proving the success of the Euro standards in diminishing NOx emissions. London presented a modest decrease in urban background NOx concentrations (−1.3% yr−1), while EFNOx remained rather constant at the curbside site (Marylebone Road) due to the increase in public bus traffic. NO2 primary emissions —that are not regulated— increased until 2008–2010, which also reflected in the ambient concentrations. This increase was associated with a strong dieselization process and the introduction of new after-treatment technologies that targeted the emission reduction of other species (e.g., greenhouse gases or particulate matter). Thus, while regulations on ambient concentrations of specific species have positive effects on human health, the overall outcomes should be considered before widely adopting them. Emission inventories for the on-road transportation sector should include EFNOx derived from real-world measurements, particularly in urban settings.

Reducing Vehicle Glass Sensitivity to Turbulent Pressure

<div class="section abstract"><div class="htmlview paragraph">Vehicle interior wind noise is typically managed through the overall exterior geometry of the vehicle, mirror shape and mounting location, sealing features and glass thickness and damping. Prior research has distinguished between contribution of fluctuating pressure due to air turbulence as compared to acoustic pressure to a passenger vehicles exterior at highway speeds. Because of the large difference in propagation speed between turbulent and acoustic pressure for on-road passenger vehicles, the structural response of the glass to turbulent versus acoustic pressure is not the same. The acoustic coincidence frequency of door glass is typically in the 2-3 kHz range. Turbulent coincidence frequency is much lower, and the effective transmission loss (TL) of the glass depends on the mix of turbulent and acoustic pressure on the exterior surface of the glass. Structural changes to the perimeter of the glass itself can reduce turbulent sensitivity, which, at highway speeds, can reduce low frequency noise due to turbulent pressure excitation of door glass vibration modes.</div></div>

Lane change strategy analysis and recognition for intelligent driving systems based on random forest

The development of intelligent connected technology provides a platform for multidimensional information interaction and makes the recognition of a specific lane change strategy become a reality. Intelligent driving systems cannot make human-like decisions by only recognizing left or right lane change behaviors; in fact, poor environmental cognitive competence is the main cause of accidents of intelligent driving systems during road tests. Therefore, the recognition of specific lane change strategies can provide support for safety performance improvement. This research aims to identify the different lane change strategies of a subject vehicle with a rear approaching vehicle in the target lane. For the purpose of acquiring different strategies, a total of 42 experienced drivers including 37 males and 5 females participated in the on-road vehicle experiments, and we divided lane change strategies into three categories: mandatory, yielding, and waiting for lane change. The lane change duration time, lane crossing time, and characteristic parameters including distance to lane line, steering wheel angle, relative distance, and relative speed under different lane change strategies were compared and analyzed. Then the random forest classifier was employed to construct a lane change strategy identification model. Results indicated that the maximum value of global recognition accuracy under three input parameters reached 88.78% when the time window was 1.2 s, which was higher than the maximum value of recognition accuracy under four input parameters, but the identification time was 0.1 s later than the model with four input parameters. In addition, the recognition performance of the random forest model, attention-bidirectional long short-term memory (Atten-BiLSTM) model and genetic algorithm-support vector machine (GA-SVM) model was compared. The results demonstrated that the global recognition accuracy and identification time of the proposed model were better than that of GA-SVM model under different input parameters, and the performance was comparable to the Atten-BiLSTM model. The findings provide the basis for the intelligent driving system to make more human-like decisions and improve safety performance.

Air pollutant emissions from on-road vehicles and their control in Inner Mongolia, China

Vehicle emissions have been investigated in many developed areas; however, little attention has been paid to underdeveloped regions. This study conducts a comprehensive investigation on the vehicle emissions in Inner Mongolia, an area with low economic development in China. In 2019, total vehicle emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and inhalable particles (PM10) are 211.04, 100.83, 38.96, and 4.43 Gg, respectively. Gasoline vehicles are the main contributors of CO and VOCs, while diesel vehicles contribute more NOx and PM10. Vehicle emissions are various in different regions and times. The variations in vehicle emissions are forecasted from 2020 to 2025 under the business-as-usual scenario (BAU), controls on in-use vehicles (CIV), vehicle population constraint (VPC), public transportation improvement (PTI), new energy vehicle promotion (NEV), and integrated scenario (IS). In addition, reduction effects, cost of implementation and energy consumption are analyzed for all the scenarios. At present, the CIV and VPC are suggested to be adopted primarily in Inner Mongolia, while other policies need to be postponed according to the analysis results.

Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain

In recent years, both developed and developing countries have witnessed an increase in the number of traffic accidents. Aside from a significant rise in the overall number of on-road commercial and non-commercial vehicles, advancements in transportation infrastructure and on-road technologies may result in road accidents, which generally result in high mortality. More than half of these fatalities are the result of delayed response by medical and rescue personnel. If an accident site receives quick medical treatment, an accident victim's chances of survival may improve considerably. Based on the IoT-based multiple-level vehicle environment, this study proposes a low-cost accident detection and alarm system. Vehicles are equipped with a "Black Box" board unit and an accident location identification module for the Global Positioning System (GPS), in addition to mechanical sensors (accelerometer, gyroscope) for accurate accident detection. This study has evaluated the proposed system with average packet delivery ratio (PDR) vs. relay nodes. Our simulation results have evaluated the evolution of relay nodes in the mobile / sensor node through internet gateway. It has also been demonstrated that the packet delivery ratio is inversely related to the incremental number of relay nodes.

Measurement report: Emissions of intermediate-volatility organic compounds from vehicles under real-world driving conditions in an urban tunnel

Abstract. Intermediate-volatility organic compounds (IVOCs) emitted from vehicles are important precursors to secondary organic aerosols (SOAs) in urban areas, yet vehicular emission of IVOCs, particularly from on-road fleets, is poorly understood. Here we initiated a field campaign to collect IVOCs with sorption tubes at both the inlet and the outlet in a busy urban tunnel (>30 000 vehicles per day) in south China for characterizing emissions of IVOCs from on-road vehicles. The average emission factor of IVOCs (EFIVOCs) was measured to be 16.77±0.89 mg km−1 (average ±95 % CI, confidence interval) for diesel and gasoline vehicles in the fleets, and based on linear regression, the average EFIVOCs was derived to be 62.79±18.37 mg km−1 for diesel vehicles and 13.95±1.13 mg km−1 for gasoline vehicles. The EFIVOCs for diesel vehicles from this study was comparable to that reported previously for non-road engines without after-treatment facilities, while the EFIVOCs for gasoline vehicles from this study was much higher than that recently tested for a China V gasoline vehicle. IVOCs from the on-road fleets did not show significant correlation with the primary organic aerosol (POA) or total non-methane hydrocarbons (NMHCs) as results from previous chassis dynamometer tests. Estimated SOA production from the vehicular IVOCs and VOCs surpassed the POA by a factor of ∼2.4, and IVOCs dominated over VOCs in estimated SOA production by a factor of ∼7, suggesting that controlling IVOCs is of greater importance to modulate traffic-related organic aerosol (OA) in urban areas. The results demonstrated that although on-road gasoline vehicles have much lower EFIVOCs, they contribute more IVOCs than on-road diesel vehicles due to its dominance in the on-road fleets. However, due to greater diesel than gasoline fuel consumption in China, emission of IVOCs from diesel engines would be much larger than that from gasoline engines, signaling the overwhelming contribution of IVOC emissions by non-road diesel engines in China.

Exploratory Analysis of Pedestrian Road Trauma in Finland

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.

SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles.

The Internet of vehicles (IoV) is a rapidly emerging technological evolution of Intelligent Transportation System (ITS). This paper proposes SafeDrive, a dynamic driver profile (DDP) using a hybrid recommendation system. DDP is a set of functional modules, to analyses individual driver’s behaviors, using prior violation and accident records, to identify driving risk patterns. In this paper, we have considered three synthetic data-sets for 1500 drivers based on their profile information, risk parameters information, and risk likelihood. In addition, we have also considered the driver’s historical violation/accident data-set records based on four risk-score levels such as high-risk, medium-risk, low-risk, and no-risk to predict current and future driver risk scores. Several error calculation methods have been applied in this study to analyze our proposed hybrid recommendation systems’ performance to classify the driver’s data with higher accuracy based on various criteria. The evaluated results help to improve the driving behavior and broadcast early warning alarm to the other vehicles in IoV environment for the overall road safety. Moreover, the propoed model helps to provide a safe and predicted environment for vehicles, pedestrians, and road objects, with the help of regular monitoring of vehicle motion, driver behavior, and road conditions. It also enables accurate prediction of accidents beforehand, and also minimizes the complexity of on-road vehicles and latency due to fog/cloud computing servers.

Variations in concentration and solubility of iron in atmospheric fine particles during the COVID-19 pandemic: An example from China

Iron (Fe) in the atmosphere can affect atmospheric chemical processes and human health. When deposited into oceans, it can further influence phytoplankton growth. These roles of Fe fundamentally depend on its concentration and solubility. However, the sources of aerosol Fe and controlling factors of Fe solubility in megacities remain poorly understood. The outbreak of the COVID-19 pandemic causes large changes in human activities, which provides a unique opportunity to answer these key issues. Field observations were conducted before, during, and after the COVID-19 lockdown in Hangzhou, China. Our results show that in the COVID-19 lockdown stage, the concentrations of total Fe (FeT, 75.0 ng m−3) and soluble Fe (FeS, 5.1 ng m−3) in PM2.5 decreased by 78% and 62%, respectively, compared with those (FeT 344.7 ng m−3, FeS 13.5 ng m−3) in the pre-lockdown stage. The sharp reduction (81%) in on-road vehicles was most responsible for the aerosol Fe decrease. Surprisingly, the Fe solubility increased by a factor of 1.9, from 4.2% in the pre-lockdown stage to 7.8% in the COVID-19 lockdown stage. We found that the atmospheric oxidizing capacity was enhanced after lockdown restrictions were implemented, which promoted the formation of more acidic species and further enhanced the dissolution of aerosol Fe.

Emissions and light absorption of carbonaceous aerosols from on-road vehicles in an urban tunnel in south China

With changing numbers, compositions, emission standards and fuel quality of on-road vehicles, it is imperative to accordingly characterize and update vehicular emissions of carbonaceous aerosols for better understanding their health and climatic effects. In this study, a 7-day field campaign was conducted in 2019 in a busy urban tunnel (>30,000 vehicles day−1) in south China with filter-based aerosol samples collected every 2 h at both the inlet and the outlet for measuring carbonaceous aerosols and their light absorbing properties. Observed fleet average emission factor (EF) of total carbon (TC) was 13.4 ± 8.3 mg veh−1 km−1, and 17.4 ± 11.3 mg veh−1 km−1 if electric and LPG-driven vehicles were excluded; and fleet average EF of organic carbon (OC) and elemental carbon (EC) was 8.5 ± 6.6 and 4.9 ± 2.6 mg veh−1 km−1 (11.0 ± 8.8 and 6.3 ± 3.6 mg veh−1 km−1 if excluding electric and LPG vehicles), respectively. Regression analysis revealed an average TC-EF of 319.8 mg veh−1 km−1 for diesel vehicles and 2.1 mg veh−1 km−1 for gasoline vehicles, and although diesel vehicles only shared ~4% in the fleet compositions, they still dominate on-road vehicular carbonaceous aerosol emissions due to their over 150 times higher average TC-EF than gasoline vehicles. Filter-based light absorption measurement demonstrated that on average brown carbon (BrC) could account for 19.1% of the total carbonaceous light absorption at 405 nm, and the average mass absorption efficiency of EC at 635 nm and that of OC at 405 nm were 5.2 m2 g−1 C and 1.0 m2 g−1 C, respectively.

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

The social costs of health- and climate-related on-road vehicle emissions in the continental United States from 2008 to 2017

Local and state policymakers have become increasingly interested in developing policies that both reduce greenhouse gas (GHG) emissions and improve local air quality, along with public health. Interest in developing transportation-related policies has grown as transportation became the largest contributing sector to GHG emissions in the United States in 2017. Information on current emissions and health impacts, along with trends over time, is helpful to policymakers who are developing strategies to reduce emissions and improve public health, especially in areas with high levels of transportation-related emissions. Here, we provide a comprehensive assessment of the public health and climate social costs of on-road emissions by linking emissions data generated by the U.S. Environmental Protection Agency to reduced complexity models that provide impacts per ton emitted for pollutants which contribute to ambient fine particulate matter, and the social costs of GHG emissions from on-road transportation. For 2017, social costs totaled $184 billion (min: $78 billion; max: $280 billion) for all on-road emissions from the eight health and climate pollutants that we assessed in the continental U.S. (in $2017 USD). Within this total social cost estimate, health pollutants constituted $93 billion of the social costs (min: $52 billion; max: $146 billion), and climate pollutants constituted $91 billion (min: $26 billion; max: $134 billion). The majority of these social costs came from CO2 followed by NO x emissions from privately owned individual vehicles in urban counties (CO2 contributed $51 billion and NO x contributed $16 billion in social costs from individual vehicles in urban counties). However, it is important to note that not all the attention should be placed solely on individual vehicles. Although the climate social costs of individual vehicle emissions are higher than those from commercial vehicles in urban counties (by two to eight times depending on the climate pollutant), the health social costs of individual vehicle emissions are roughly equal to those from commercial vehicles in urban counties. Regardless of each pollutant’s contributions to the social costs, the highest social benefits from reducing 1 ton of CO2 and its co-pollutants would occur in urban counties, given their high population density.