No abstract

Vehicle manufacturers are required to reduce their European sales-weighted emissions to 95 g CO 2 /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.

Consumer, legal, and technological factors influence the design, performance, and emissions of light-duty vehicles (LDVs). This work examines how design choices made by manufacturers for the UK market result in emissions and performance of vehicles throughout the past decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011). LDV fuel consumption, CO 2 emissions, and performance are compared across different combinations of air and fuel delivery system using vehicle performance metrics of power density and time to accelerate from rest to 100 km/h (62 mph, t z-62 ). Increased adoption of direct injection and turbocharging technologies helped reduce spark ignition (SI, gasoline vehicles) and compression ignition (CI, diesel vehicles) fuel consumption by 22% and 19%, respectively, over the decade. These improvements were largely achieved by increasing compression ratios in SI vehicles (3.6%), turbocharging CI vehicles, and engine downsizing by 5.7-6.5% across all technologies. Simultaneously, vehicle performance improved, through increased engine power density resulting in greater acceleration. Across the decade, t z-62 fell 9.4% and engine power density increased 17% for SI vehicles. For CI vehicles, t z-62 fell 18% while engine power density rose 28%. Greater fuel consumption reductions could have been achieved if vehicle acceleration was maintained at 2001 levels, applying drive train improvements to improved fuel economy and reduced CO 2 emissions. Fuel consumption and CO 2 emissions declined at faster rates once the European emissions standards were introduced with SI

While the UK has committed to reduce CO emissions to 80% of 1990 levels by 2050, transport accounts for nearly a fourth of all emissions and the degree to which decarbonization 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 CO 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 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 CO 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 CO emissions by less (42 ± 10% reduction) than concerted internal combustion engines improvements. However, if the grid decarbonizes, electric vehicles reduce emissions by 45 ± 9% with further reduction potential to 2050.

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 powertain inputs to understand the role their technology can play in a fleet under the influence of consumer behaviour change, external incentives and policy objectives.Fleet composition and adoption of new technology are modelled in the literature using three main 17 approaches: diffusion rates; agent-based; and consumer choice [? ]. Diffusion rate models aim to identify 18 the product life cycle and require both the ultimate market potential and peak year of sales to be known a 19 priori. The other two methods consider consumers from the bottom up as individuals or groups, respectively. 20Both models require knowledge of consumer preferences. These are represented as a set of weighted attributes 21 to determine the probability that one of a number of vehicles will be chosen. However, some attributes for 22 novel powertrains can be difficult to obtain when there is little historical sales data available. 23This work uses fleet diversity to infer the net result of all consumer preferences from the top down. 24Therefore, instead of forecasting new fleet composition based on a hos...