On-road Vehicles Research Articles

Rapid assessments of light-duty gasoline vehicle emissions using on-road remote sensing and machine learning

In-time and accurate assessments of on-road vehicle emissions play a central role in urban air quality and health policymaking. However, official insight is hampered by the Inspection/Maintenance (I/M) procedure conducted in the laboratory annually. It not only has a large gap to real-world situations (e.g., meteorological conditions) but also is incapable of regular supervision. Here we build a unique dataset including 103,831 light-duty gasoline vehicles, in which on-road remote sensing (ORRS) measurements are linked to the I/M records based on the vehicle identification numbers and license plates. On this basis, we develop an ensemble model framework that integrates three machining learning algorithms, including neural network (NN), extreme gradient boosting (XGBoost), and random forest (RF). We demonstrate that this ensemble model could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In particular, the model performs quite well for the passing vehicles under normal conditions (i.e., lower VSP (<18 kw/t), temperature (6–32 °C), relative humidity (<80%), and wind speed (<5 m/s)). Together with the current emission standard, we identify a large number of the ‘dirty’ (2.33%) or ‘clean’ (74.92%) vehicles in the real world. Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions. This approach framework provides a valuable opportunity to reform the I/M procedures globally and mitigate urban air pollution deeply.

A review of on-road vehicle emission inventory

The large increase in the on-road vehicle population in China has raised sustainability concerns regarding air pollution prevention, energy conservation, and climate change mitigation. Vehicle emission inventory is an irreplaceable tool to characterize the temporal and spatial distribution of the air pollutant and provide guidance to the policy makers with effective vehicle emission controls. This review paper reviewed two kinds of typical vehicle emission inventories. The top-down vehicle emission inventories is calculated based on the static datasets (e.g., vehicle population, vehicle kilometer traveled, and fuel consumption). These inventories could track historical emissions abatement progress and examine potential benefits from future regulations. The technological evolution in intelligent transportation systems have facilitated emission inventories to satisfy the increasing sophisticated management demand. The bottom-up link-level vehicle emission inventories are development based on the availability of the real-world traffic profiles. To simulate the temporal and spatial patterns with high-resolution, traffic demand model and machine learning methods are employed to elucidate traffic emissions.

A Comparative Total Cost of Ownership Analysis of Heavy Duty On-Road and Off-Road Vehicles Powered by Hydrogen, Electricity, and Diesel

A Comparative Total Cost of Ownership Analysis of Heavy Duty On-Road and Off-Road Vehicles Powered by Hydrogen, Electricity, and Diesel

An Overview on Medium Voltage Grid Integration of Ultra-Fast Charging Stations: Current Status and Future Trends

The emphasis on clean and green technologies to curtail greenhouse gas emissions due to fossil fuel-based economies has originated the shift towards electric mobility. As on-road electric vehicles (EVs) have shown exponential growth over the last decade, so have the charging demands. The provision of charging facilities from the low-voltage network will not only increase the distribution system's complexity and dynamics but will also challenge its operational capabilities, and large-scale upgrades will be required to meet the inevitably increasing charging demands. An ultra-fast (UF) charging infrastructure that replicates the gasoline refueling network is urgently needed to facilitate a seamless transition to EVs and ensure smooth operation. This paper presents a review of state-of-the-art DC fast chargers, the charging infrastructure's current status, motivation, and challenges for medium-voltage (MV) UF charging stations (UFCS). Furthermore, we consider the possible UFCS architectures and suitable power electronics topologies for UF charging applications. To address the peak formation issues in the daily load profile and high operational expenses of UFCSs, integration of renewable energy sources and energy storage systems due to their technological and economic benefits is being considered. The benefits of line frequency transformer (LFT) replacement with a solid-state transformer (SST),SST models, SST-based UF chargers, and MV SST-based UFCS architectures, as well as related MV active front-end and back-end power electronics topologies, are presented. Finally, the application of microgrids&#x0027; hierarchical control architecture is considered for chargers and system-level control and management of UFCSs.

A Comparative Total Cost of Ownership Analysis of Heavy Duty On-Road and Off-Road Vehicles Powered by Hydrogen, Electricity, and Diesel

A Comparative Total Cost of Ownership Analysis of Heavy Duty On-Road and Off-Road Vehicles Powered by Hydrogen, Electricity, and Diesel

Stabilization of vertical motion of a vehicle on bumpy terrain using deep reinforcement learning*

Stabilizing vertical dynamics for on-road and off-road vehicles is an important research area that has been looked at mostly from the point of view of ride comfort. The advent of autonomous vehicles now shifts the focus more towards developing stabilizing techniques from the point of view of onboard proprioceptive and exteroceptive sensors whose real-time measurements influence the performance of an autonomous vehicle. The current solutions to this problem of managing the vertical oscillations usually limit themselves to the realm of active suspension systems without much consideration to modulating the vehicle velocity, which plays an important role by the virtue of the fact that vertical and longitudinal dynamics of a ground vehicle are coupled. The task of stabilizing vertical oscillations for military ground vehicles becomes even more challenging due lack of structured environments, like city roads or highways, in offroad scenarios. Moreover, changes in structural parameters of the vehicle, such as mass (due to changes in vehicle loading), suspension stiffness and damping values can have significant effect on the controller's performance. This demands the need for developing deep learning based control policies, that can take into account an extremely large number of input features and approximate a near optimal control action. In this work, these problems are addressed by training a deep reinforcement learning agent to minimize the vertical acceleration of a scaled vehicle travelling over bumps by controlling its velocity.

Prediction of dynamic characteristics of four cylinder engine chassis using Finite element analysis approach

It has been observed that an engine vibration is a significant factor in on-road passenger vehicles. The issue that needs to be addressed from the standpoint of the customers, and in light of this issue, the dynamic characteristics of the four-cylinder engine chassis are analysed using Finite Element Analysis (FEA) to determine the natural frequency. The modal analysis was performed and the natural frequencies were observed in various modes for low frequency vibration in damped as well as undamped situations and then, the corresponding displacements were compared. The results evidenced that the engine mount made of an elastomeric material can act as the vibration seal and it would help to suppress the transfer of vibration to the chassis, significantly. Specifically, the findings have proved that the elastomeric mount acted as the damper and suppressed the displacement and deformation by 17% and 26%, respectively.

Designing Electronic Traffic Information Acquisition System Using Deep Learning and Internet of Things

In implementing the Intelligent Traffic Monitoring System (ITMS), timely and effective access to road traffic information is an essential link. It requires an effective traffic Information Acquisition System (IAS) to collect real-time data and transmit the collected information to the background for processing. Therefore, this paper studies on-road vehicle information recognition based on Deep Learning (DL). Firstly, a framework of traffic IAS is proposed. Then, an improved MT-GooGleNet model based on Convolutional Neural Network (CNN) is proposed to locate and recognize vehicles in traffic images. Finally, the performance of the model is analyzed by simulation. The experimental results of vehicle position recognition show that the classification accuracy of Multi-Task (MT)-GooGleNet after fine-tuning is 99.5%. Compared with other models, the MT-GooGleNet model proposed is the best in vehicle position recognition, and its positioning accuracy is very high. The results of vehicle identification show that after data enhancement and pre-training, the testing set accuracy of the MT-GooGleNet model is 79.96%. The results show that the model&#x2019;s accuracy has been dramatically improved after processing. The research provides a reference for establishing IAS in the future.

Spatial variation and driving mechanism of polycyclic aromatic hydrocarbons (PAHs) emissions from vehicles in China

Rapid motorization has made vehicles become one of the major sources of air pollution and poses substantial risk to human health. Accurate estimation of spatiotemporal variation and driving factors of vehicle emissions will be valuable for pollution control and public health protection. Here, taking polycyclic aromatic hydrocarbons (PAHs) as an example, a dynamic vehicle emission model (DVEM) was developed to assess vehicle emissions. The inventories of vehicle emissions from 2002 to 2017 were estimated under different emission standards and vehicle kilometers traveled for all types of vehicles in China. Vehicle PAHs emissions peaked 1586.85 tonne (t) in 2012 and declined by 36% in the follow-up five years because the vehicle growth has been offset by the upgraded emissions standards. There were remarkable variations among different provinces in China. The vehicle emissions are higher in eastern coastal provinces like Guangdong, Shandong, Jiangsu, and Zhejiang, while lower in the western provinces except Xinjiang. Motorcycles (44.1%) and light duty vehicles (17.8%) were the main contributors to PAHs emissions. The higher urbanization within a region, the larger its vehicle emission density. Urban road density may be linked with the number of on-road vehicles, which is the real driving factor of emissions. Therefore, integrated management should be taken by government to reduce the impacts of vehicle PAHs emissions.

Slip Detection and Control for Harvesting Machines

This paper deals with a traction control system (TCS) for combine harvesters. Off-road vehicles typically have a lower traction efficiency compared to on-road vehicles. The rising demand for high-power agricultural machines leads to a significant need for higher traction efficiency to reduce power loss. A TCS can help to increase the traction efficiency by controlling the slip ratio. Further positive effects are the reduction of soil compaction and longitudinal stability of the vehicle. In this paper a combine harvester model is introduced to describe the dynamic interaction between drivetrain and vehicle longitudinal dynamics which include slip. This model depends on several parameters, which can be estimated online along with the determination of the maximal slip ratio. The plant model is successfully validated using field test measurements. A PID traction controller and a sliding-mode traction controller are designed and compared. Two control concepts are evaluated utilizing this model and these controllers. Simulation results show the effectiveness and efficiency of the proposed TCS.

In-Situ Measurement-Based Intermediate Volatility Organic Compound Emission Inventory from On-Road Vehicle Exhaust in China

In-Situ Measurement-Based Intermediate Volatility Organic Compound Emission Inventory from On-Road Vehicle Exhaust in China

Deep Reinforcement Learning Based Adaptation of Pure-Pursuit Path-Tracking Control for Skid-Steered Vehicles

The growing need for autonomous vehicles in the offroad space raises certain complexities that need to be considered more rigorously in comparison to onroad vehicle automation. Popular path control frameworks in onroad autonomy deployments such as the pure-pursuit controller use geometric and kinematic motion models to generate reference trajectories. However in the offroad settings these controllers, despite their merits (low design and computation requirements), could compute dynamically infeasible trajectories as several of the nominal assumptions made by these models don't hold true when operating in a 2.5D terrain. Outside of the notable challenges such as uncertainties and non-linearities/disturbances introduced by the unknown/unmapped 2.5D terrains, additional complexities arise from the use of vehicle architectures such as the skid-steer that experience lateral skidding for achieving simple curvilinear motion. Additionally, linear models of skid-steer vehicles often consist of high modeling uncertainty which renders traditional linear optimal and robust control techniques inadequate given their sensitivity to modeling errors. Nonlinear MPC has emerged as an upgrade, but needs to overcome realtime deployment challenges (including slow sampling time, design complexity, and limited computational resources). This provides an unique opportunity to utilize data-driven adaptive control methods in tailored application spaces to implicitly learn and hence compensate for the unmodeled aspects of the robot operation. In this study, we build an adaptive control framework called Deep Reinforcement Learning based Adaptive Pure Pursuit (DRAPP) where the base structure is that of a geometric Pure-Pursuit (PP) algorithm which is adapted through a policy learned using Deep Reinforcement Learning (DRL). An additional law that enforces a mechanism to account for the rough terrain is added to the DRL policy to prioritize smoother reference-trajectory generation (and thereby more feasible trajectories for lower-level controllers). The adaptive framework converges quickly and generates smoother references relative to a pure 2D-kinematic path tracking controller. This work includes extensive simulations and a bench marking of the DRAPP framework against Nonlinear Model Predictive Control (NMPC) that is an alternate popular choice in literature for this application.

Reconciling the bottom-up methodology and ground measurement constraints to improve the city-scale NMVOCs emission inventory: A case study of Nanjing, China

Reliable emission estimate of non-methane volatile organic compounds (NMVOCs) is important for understanding the atmospheric chemistry and formulating control policy of ozone (O3). In this study, a speciated emission inventory of anthropogenic NMVOCs was developed with the refined “bottom-up” methodology and best available information of individual sources for Nanjing in 2017. The total NMVOCs emissions were calculated at 163.2 Gg. It was broken down into the emissions of over 500 individual species and aromatics took the largest fraction (33.3% of the total emissions). Meanwhile, 105 compounds were measured at 5 sites representing different functional zones of Nanjing for one year. The annual mean concentration of totally 105 species varied from 48.5 ppbv to 63.7 ppbv, and alkanes was the most abundant group with its mass fractions ranging 37.2–40.1% at different sites. Constrained by the emission ratios of individual species versus carbon monoxide (CO) based on ground measurement, the total emissions of 105 species (NMVOCs-105) was estimated at 195.6 Gg, 81.1% larger than the bottom-up estimate of NMVOCs-105 (108.0 Gg). The constrained emissions indicated an overestimation of aromatics and underestimation of OVOCs and halocarbons in the bottom-up emission inventory because of the uncertainties in source profiles. O3 simulation with Community Multi-scale Air Quality (CMAQ) model was conducted for January, April, July and October in 2017 to evaluate the bottom-up and constrained emission estimates. The mean normal bias (MNB) and mean normal error (MNE) values were generally within the criteria (MNB ≤ ±15% and MNE ≤ 30%) for both inventories. The model performance was improved when the constrained estimates were applied, indicating the benefit of ground observation constraints on NMVOCs emission estimation and O3 simulation. Based on the O3 formation potential (OFP), 12 key NMVOCs species mainly from surface coating, on-road vehicles and oil exploitation and refinery were identified as the priority compounds for O3 reduction.

Impacts of sectoral, regional, species, and day-specific emissions on air pollution and public health in Washington, DC

We present a novel source attribution approach that incorporates satellite data into GEOS-Chem adjoint simulations to characterize the species-specific, regional, and sectoral contributions of daily emissions for 3 air pollutants: fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2). This approach is implemented for Washington, DC, first for 2011, to identify urban pollution sources, and again for 2016, to examine the pollution response to changes in anthropogenic emissions. In 2011, anthropogenic emissions contributed an estimated 263 (uncertainty: 130–444) PM2.5- and O3-attributable premature deaths and 1,120 (391–1795) NO2 attributable new pediatric asthma cases in DC. PM2.5 exposure was responsible for 90% of these premature deaths. On-road vehicle emissions contributed 51% of NO2-attributable new asthma cases and 23% of pollution-attributable premature deaths, making it the largest contributing individual sector to DC’s air pollution–related health burden. Regional emissions, originating from Maryland, Virginia, and Pennsylvania, were the most responsible for pollution-related health impacts in DC, contributing 57% of premature deaths impacts and 89% of asthma cases. Emissions from distant states contributed 34% more to PM2.5 exposure in the wintertime than in the summertime, occurring in parallel with strong wintertime westerlies and a reduced photochemical sink. Emission reductions between 2011 and 2016 resulted in health benefits of 76 (28–149) fewer pollution-attributable premature deaths and 227 (2–617) fewer NO2-attributable pediatric asthma cases. The largest sectors contributing to decreases in pollution-related premature deaths were energy generation units (26%) and on-road vehicles (20%). Decreases in NO2-attributable pediatric asthma cases were mostly due to emission reductions from on-road vehicles (63%). Emission reductions from energy generation units were found to impact PM2.5 more than O3, while on-road vehicle emission reductions impacted O3 proportionally more than PM2.5. This novel method is capable of capturing the sources of urban pollution at fine spatial and temporal scales and is applicable to many urban environments, globally.

Optimal Solutions for Underwater Capacitive Power Transfer.

Capacitive power transfer (CPT) has attracted attention for on-road electric vehicles, autonomous underwater vehicles, and electric ships charging applications. High power transfer capability and high efficiency are the main requirements of a CPT system. This paper proposes three possible solutions to achieve maximum efficiency, maximum power, or conjugate-matching. Each solution expresses the available load power and the efficiency of the CPT system as functions of capacitive coupling parameters and derives the required admittance of the load and the source. The experimental results demonstrated that the available power and the efficiency decrease by the increasing of the frequency from 300 kHz to 1 MHz and the separation distance change from 100 to 300 mm. The maximum efficiency solution gives 83% at 300 kHz and a distance of 100 mm, while the maximum power solution gives the maximum normalized power of 0.994 at the same frequency and distance. The CPT system can provide a good solution to charge electric ships and underwater vehicles over a wide separation distance and low-frequency ranges.

Brake and tire particles measured from on-road vehicles: Effects of vehicle mass and braking intensity

Vehicle exhaust emissions have been decreasing due to stricter regulations and advancements in control strategies. However, non-exhaust emissions from brake and tire wear have not been extensively regulated in the past, and their relative contribution to particulate matter (PM) in urban areas is increasing. We examined the effect of a vehicle's mass and braking intensity on brake and tire particles based on on-road data collected from three different types of vehicles under real-world driving conditions. PM 2.5 and PM 10 concentrations and particle size distributions were measured near the center and rear of the right front wheel, respectively. During the braking, the highest peaks in brake PM 2.5 (520–4280 μg/m 3 ) and PM 10 (950–8420 μg/m 3 ) concentrations were observed from the heaviest vehicle, while the lowest peaks in brake PM 2.5 (250–2440 μg/m 3 ) and PM 10 (430–3890 μg/m 3 ) concentrations were observed from the lightest vehicle. Similarly, the observed peaks in tire PM 2.5 (340–4750 μg/m 3 ) and PM 10 (810–8290 μg/m 3 ) concentrations of the heaviest vehicle were shown to be the highest among the test vehicles, while the peaks in tire PM 2.5 (220–2150 μg/m 3 ) and PM 10 (370–3840 μg/m 3 ) concentrations of the lightest vehicle were lower than other vehicles. A statistically significant difference in the peak values of PM 2.5 and PM 10 concentrations was observed between the heaviest and lightest vehicles for both brake and tire particles. The braking deceleration rate was found to be an important factor in predicting the peaks in PM 2.5 and PM 10 concentrations during major braking events for all three test vehicles. Brake particles showed a unimodal mass size distribution with a mode diameter of 3–4 μm, while tire particles showed a slightly larger mode diameter of 4–5 μm. The finding of this study provides insight into the effects of driving conditions and vehicle mass on brake and tire emissions. • Brake and tire particles were measured from three vehicles tested on-road. • Heavier vehicles emit higher levels of brake and tire particles. • Heavy braking can lead to elevated brake and tire PM concentrations.

Routing on-road heavy vehicles for alleviating greenhouse gas emissions

The increasing demand for freight transport leads to rising fuel consumption and additional greenhouse gas (GHG) emissions. This study aims to evaluate the impact of route optimization of heavy vehicles on fuel consumption and GHG emissions. The regular routes of two piloted trucks were extracted and compared with the optimized distances from the simulated model by Network Analyst in Arc GIS. By using Network Analyst and combining it with the Fuel Consumption Rate (FCR) and GHG emission, this study takes a step forward to quantify the amount of fuel usage and GHG emitted from heavy vehicles in the optimized routes versus their regular paths. For this purpose, two different trucks of Blacktown City (Australia) were selected: a waste collector and a tree maintenance truck. The result of the study shows that using the optimized route instead of the regular one can reduce the distance driven by tree maintenance truck by 60%, fuel consumption by 62%, and GHG emissions by 62% per month. In addition, the optimized route shows a 10% reduction in distance travelled, 11% in fuel consumption, and 10% in GHG emission per month. This study also compares fixed-mission and changing-mission trucks. . The results in this part show that the fixed mission truck is much more efficient. Therefore, implementing any efficient vehicle routing system will have considerable impacts on the changing-mission trucks.

Evaporation process dominates vehicular NMVOC emissions in China with enlarged contribution from 1990 to 2016

Non-methane volatile organic compounds (NMVOC) are important precursors of ozone and secondary organic aerosols in PM2.5 (particulate matter with aerodynamic diameters smaller than 2.5 μm), both of which cause severe climate, ecosystem, and human health damages. As one of the major anthropogenic sources, onroad vehicles are subject to relatively large errors and uncertainties in the estimation of NMVOC emissions due to complicated methods and parameters involved and a lack of comprehensive evaluation of influencing factors. Here, based on our previous work with necessary improvement, we estimate China’s vehicular NMVOC emissions by county and by month during 1990–2016 with a consideration of meteorological influence on the spatial-temporal dynamics of emission factors. Our estimate suggests that vehicular NMVOC emissions in China have peaked around 2008 and then declined up to 2016 with an enlarged contribution of the evaporative process to vehicular NMVOC emissions. Vehicular NMVOC emissions have been dominated by the evaporative process at present. Meteorological factors alter spatial-temporal distributions of NMVOC emissions, especially evaporative emissions, which are enhanced in South China and in summer. Emissions and ozone formation potential of the major chemical groups (i.e. Alkenes, Aromatics, and Alkanes) also increase substantially due to meteorological influences. Our analysis suggests that mitigation strategies for vehicle pollutions should be designed based on a sophisticated emission inventory accounting for the meteorological impact on emission factors to correct the potential underestimation of NMVOC emissions, especially those from the evaporative process.

A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Abstract. Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.