Real-world environmental impacts from modern passenger vehicles operating in urban settings

Real-world testing of a set of modern vehicles show most petrols meet their Euro standards for nitrous oxides (NO$_{x}$), while most diesel vehicles exceed them. However, that some diesel vehicles met their Euro standards implies exceedances are not peculiar to the fuel. Likewise, the compliance of the tested petrol vehicles with the standard does not mean all petrol vehicles do. Engine maps were synthesised which reproduced trip level emissions to within 10% of that gathered under real-world driving conditions. Average velocity alone, such as what is used in COPERT, is a poor predictor of emissions. Stepwise linear models showed NO$_{x}$ emissions could be predicted accurately by incorporating other metrics, such as maximum deceleration and the variance of velocity over the driving cycle. The models were validated on three driving cycles where all vehicles met their Euro standards, save Euro 6 diesel vehicles on the US highway cycle. COPERT overestimated NO$_{x}$ from all vehicles. More work is required to combine driving cycle metrics with vehicle characteristics, such as mass and peak engine torque, to identify the conditions under which vehicles exceed their Euro limits.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by WIT Press

Quantifying the role of vehicle size, powertrain technology, activity and consumer behaviour on new UK passenger vehicle fleet energy use and emissions under different policy objectives

This paper quantifies the impacts of policy objectives on the composition of an optimum new passenger vehicle fleet. The objectives are to reduce individually absolute energy use and associated emissions of CO$_2$, NO$_x$ and PM$_{2.5}$. This work combines a top down, diversity-led approach to fleet composition with bottom-up models of 23 powertrain variants across nine vehicle segments. Changing the annual distance travelled only led to the smallest change in fleet composition because driving less mitigated the need to shift to smaller vehicles or more efficient powertrains. Instead, managing activity led to a ‘re-petrolisation’ of the fleet which yielded the largest reductions in emissions of NO$_x$ and PM$_{2.5}$. The hybrid approach of changing annual distance travelled and increasing willingness to accept longer payback times incorporates management of vehicle activity with consumers’ demand for novel vehicle powertrains. Combining these changes in behaviour, without feebates, allowed the hybrid approach to return the largest reductions in energy use and CO$_2$ emissions. Introducing feebates makes low-emitting vehicles more affordable and represents a supply side push for novel powertrains. The largest reductions in energy use and associated emissions occurred without any consumer behaviour change, but required large fees (£79–99 per g CO$_2$/km) on high-emitting vehicles and were achieved using the most specialised fleets. However, such fleets may not present consumers with sufficient choice to be attractive. The fleet with best diversity by vehicle size and powertrain type was achieved with both the external incentive of the feebate and consumers modifying their activity. This work has a number of potential audiences: governments and policy makers may use the framework to understand how to accommodate the growth in vehicle use with pledged reductions in emissions; and original equipment manufacturers may take advantage of the bottom-up, vehicle powertrain inputs to understand the role their technology can play in a fleet under the influence of consumer behaviour change, external incentives and policy objectives.The authors acknowledge the Engineering and Physical Sciences Research Council funding provided for this work under the Centre for Sustainable Road Freight Transport (EP/K00915X/1) and the Energy Efficient Cities Initiative (EP/ F034350/1)

Using portable emissions measurement systems (PEMS) to derive more accurate estimates of fuel use and nitrogen oxides emissions from modern Euro 6 passenger cars under real-world driving conditions

Data from portable emissions measurement systems (PEMS) and other sources have allowed the discrepancy between type approval and real-world fuel economy and nitrogen oxides (NOx) emissions to be both identified and quantified. However, a gap in the knowledge persists because identifying this discrepancy does not allow us to predict real-world fuel economy and emissions accurately. We address this gap in the knowledge using a bottom-up approach: a PEMS is used across a range of Euro 6 petrol and diesel vehicles, from which internally-consistent powertrain models are derived. These training vehicles are simulated over 20 real-world and regulated driving cycles. 26 metrics representing driving, vehicle and ambient characteristics are used to develop quantile regression (QR) models for three vehicle groups: direct-injection petrol vehicles with three way catalysts; diesel vehicles with selective catalytic reduction; and diesel vehicles with lean NOx traps. 95% prediction intervals are used to assess the predictive accuracy of the QR models from a set of validation vehicles. Across the vehicle groups, QR models for both fuel economy and NOx emissions depended on the dynamics of the driving cycles more than the engine characteristics or ambient conditions. The 95% prediction interval for fuel economy enclosed most of the observed values from the PEMS test, with similar prediction error to COPERT in most cases. The bene ts of the QR approach were more pronounced for NOx emissions, where the majority of PEMS observed data was enclosed in the 95% PI and median prediction error was up to two times lower than COPERT.EPSR

Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis

Vehicle manufacturers are required to reduce their European sales-weighted emissions to 95 g CO2/km by 2020, with the aim of reducing on-road fleet fuel consumption. Nevertheless, current fuel consumption models are not suited for the European market and are unable to account for uncertainties when used to forecast passenger vehicle energy-use. Therefore, a new methodology is detailed herein to quantify new car fleet fuel consumption based on vehicle design metrics. The New European Driving Cycle (NEDC) is shown to underestimate on-road fuel consumption in Spark (SI) and Compression Ignition (CI) vehicles by an average of 16% and 13%, respectively. A Bayesian fuel consumption model attributes these discrepancies to differences in rolling, frictional and aerodynamic resistances. Using projected inputs for engine size, vehicle mass, and compression ratio, the likely average 2020 on-road fuel consumption was estimated to be 7.6 L/100 km for SI and 6.4 L/100 km for CI vehicles. These compared to NEDC based estimates of 5.34 L/100 km (SI) and 4.28 L/100 km (CI), both of which exceeded mandatory 2020 fuel equivalent emissions standards by 30.2% and 18.9%, respectively. The results highlight the need for more stringent technological developments for manufacturers to ensure adherence to targets, and the requirements for more accurate measurement techniques that account for discrepancies between standardised and on-road fuel consumption.NEDC data measurements were supplied by CAP Consulting. The authors are also grateful to the Energy Efficient Cities Initiative and the EPSRC (EP/F034350/1) for funding this work.This is the author accepted mansucript. The final version is available via Elsevier at http://dx.doi.org/10.1016/j.apenergy.2015.03.04

Engine maps of fuel use and emissions from transient driving cycles

Air pollution problems persist in many cities throughout the world, despite drastic reductions in regulated emissions of criteria pollutants from vehicles when tested on standardised driving cycles. New vehicle emissions regulations in the European Union and United States require the use of OBD and portable emissions measurement systems (PEMS) to confirm vehicles meet specified limits during on-road operation. The resultant in-use testing will yield a large amount of OBD and PEMS data across a range of vehicles. If used properly, the availability of OBD and PEMS data could enable greater insight into the nature of real-world emissions and allow detailed modelling of vehicle energy use and emissions. This paper presents a methodology to use this data to create engine maps of fuel use and emissions of nitrous oxides (NO$_x$), carbon dioxide (CO$_2$) and carbon monoxide (CO). Effective gear ratios, gearbox shift envelopes, candidate engine maps and a set of vehicle configurations are simulated over driving cycles using the ADVISOR powertrain simulation tool. This method is demonstrated on three vehicles – one truck and two passenger cars – tested on a vehicle dynamometer and one driven with a PEMS. The optimum vehicle configuration and associated maps were able to reproduce the shape and magnitude of observed fuel use and emissions on a per second basis. In general, total simulated fuel use and emissions were within 5% of observed values across the three test cases. The fitness of this method for other purposes was demonstrated by creating cold start maps and isolating the performance of tailpipe emissions reduction technologies. The potential of this work extends beyond the creation of vehicle engine maps to allow investigations into: emissions hot spots; real-world emissions factors; and accurate air quality modelling using simulated per second emissions from vehicles operating in over any driving cycle.Engineering and Physical Sciences Research Council (Centre for Sustainable Road Freight Transport (EP/K00915X/1), Energy Efficient Cities Initiative (EP/ F034350/1)

Low-latency vision-based fiducial detection and localisation for object tracking

Real-time vision systems are widely-used in construction and manufacturing industries. A significant proportion of computational resources of such systems is used in fiducial identification and localisation for motion tracking of moving targets. The requirement is to localise a pattern in an image captured by the vision system precisely, accurately, and with a minimum available computation time. As such, this paper presents a class of patterns and, accordingly, proposes an algorithm to fulfil the requirement. Here, the patterns are designed using circular patches of concentric circles to increase the probability of detection and reduce cases of false detection. In the detection algorithm, the image captured by the vision system is first scaled down for computationally-effective processing. The scaled image is then separated by filtering only the colour components, which are made up of outer circular patches in the proposed pattern. A blob detection algorithm is then implemented for identifying inner circular patches. The inner circles are then localised in the image by using the colour information obtained. Finally, the localised pattern, along with the camera and distortion matrix of the vision system, is applied in a perspective-n-point solving algorithm to estimate the marker orientation and position in the global coordinate system. Our system shows significant enhancement in performance of fiducial detection and identification and achieves the required latency of less than ten milliseconds. Thus, it can be used for infrastructure monitoring in many applications that involve high-speed real-time vision systems

Roboteye technology for thermal target tracking using predictive control

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Thermal cameras are widely used in the fatigue analysis of mechanical structures using the thermoelastic effect. Nevertheless, such analysis is hampered due to blurry images resulting from the motion of structure-under-test. To address the issue this paper presents a system that utilizes robotic vision and predictive control. The system comprises of a thermal camera, a vision camera, a RobotEye, and a fiducial detection system. A marker is attached to a thermal target in order to estimate its position and orientation using the proposed detection system. To predict the future position of the thermal moving object, a Kalman filter is used. Finally, the Model Predictive Control (MPC) approach is applied to generate commands for the robot to follow the target. Results of the tracking by MPC are included in this paper along with the performance evaluation of the whole system. The evaluation clearly shows the improvement in the tracking performance of the development for thermal structural analysis

Lifelong training improves anti-inflammatory environment and maintains the number of regulatory T cells in masters athletes

PURPOSE: The purpose of this study was to quantify and characterize peripheral blood regulatory T cells (Tregs), as well as the IL-10 plasma concentration, in Masters athletes at rest and after an acute exhaustive exercise test. METHODS: Eighteen Masters athletes (self-reported training: 24.6 ± 1.83 years; 10.27 ± 0.24 months and 5.45 ± 0.42 h/week per each month trained) and an age-matched control group of ten subjects (that never took part in regular physical training) volunteered for this study. All subjects performed an incremental test to exhaustion on a cycle ergometer. Blood samples were obtained before (Pre), 10 min into recovery (Post), and 1 h after the test (1 h). RESULTS: Absolute numbers of Tregs were similar in both groups at rest. Acute exercise induced a significant increase in absolute numbers of Tregs at Post (0.049 ± 0.021 to 0.056 ± 0.024 × 109/L, P = 0.029 for Masters; 0.048 ± 0.017 to 0.058 ± 0.020 × 109/L, P = 0.037 for control) in both groups. Treg mRNA expression for FoxP3, IL-10, and TGF-β in sorted Tregs was similar throughout the trials in both groups. Masters athletes showed a higher percentage of subjects expressing the FoxP3 (100% for Masters vs. 78% for Controls, P = 0.038) and TGF-β (89% for Masters vs. 56% for Controls, P = 0.002) after exercise and a higher plasma IL-10 concentration (15.390 ± 7.032 for Masters vs. 2.411 ± 1.117 for control P = 0.001, ES = 2.57) at all timepoints. KLRG1 expression in Tregs was unchanged. CONCLUSION: Our findings showed that Masters athletes have elevated anti-inflammatory markers and maintain the number of Tregs, and may be an adaptive response to lifelong training.info:eu-repo/semantics/publishedVersio

How Well Do We Know the Future of CO₂ Emissions? Projecting Fleet Emissions from Light Duty Vehicle Technology Drivers

While the UK has committed to reduce CO₂ emissions to 80% of 1990 levels by 2050, transport accounts for nearly a fourth of all emissions and the degree to which decarbonisation can occur is highly uncertain. We present a new methodology using vehicle and powertrain parameters within a Bayesian framework to determine the impact of engineering vehicle improvements on fuel consumption and CO₂ emissions. Our results show how design changes in vehicle parameters (e.g. mass, engine size and compression ratio) result in fuel consumption improvements from a fleet-wide mean of 5.6 L/100 km in 2014 to 3.0 L/100 km by 2030. The change in vehicle efficiency coupled with increases in vehicle numbers and total fleet-wide activity result in a total fleet-wide reduction of 41±10% in 2030, relative to 2012. Concerted internal combustion engine improvements result in a 48±10% reduction of CO2 emissions, while efforts to increase the number of diesel vehicles within the fleet had little additional effect. Increasing plug-in and all-electric vehicles reduced CO2 emissions by less (42±10% reduction) than concerted internal combustion engines improvements. However, if the grid decarbonises, electric vehicles reduce emissions by 45±9% with further reduction potential to 2050.The authors acknowledge the UK EPSRC funding provided for this work under the Energy Efficient Cities Initiative (EP/F034350/1) and the Centre for Sustainable Road Freight Transport (EP/K00915X/1)