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

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.

Modelling transport emissions in an uncertain future: What actions make a difference?

“…Rather than focusing on a limited set of deterministic scenarios, stochastic methods can be used to consider a range of scenarios given uncertainty ranges in each of the input variables. Stochastic analyses use Monte Carlo techniques to obtain likely ranges of future emissions (Bastani et al, 2012;Martin et al, 2015;Onat et al, 2016) and are useful for defining a future solution space, from which the probability of any given outcome can be gauged. All future looking analyses are inevitably dependent upon the input variables used.…”

Environmental Effect of Bus Priority Measures Applied on a Road Network in Santiago, Chile

“…Buses are modelled in the examined network to accelerate and decelerate at 2.5 m/s 2 (absolute value). It has been shown by previous studies that the reduction of a vehicle’s acceleration can greatly reduce fuel consumption and GHG emissions and increase passenger safety ( 24 – 27 ). A bus acceleration of 1.5 m/s 2 or lower has been proven to improve comfort during bus journeys and to enable passengers to walk naturally inside the bus when searching for a seat ( 28 ).…”

Modified Dynamic Model for Longitudinal Motion of Ground Vehicles

“…It comprises four repeated ECE-15 urban driving cycles, corresponds to 0 to 780 seconds, and an extra-urban driving cycle (EUDC) that starts from 780 th second and lasts to the end of the cycle, namely 1180 th second. However, owing to the CO2 emissions gap between real-world and type approvals based on NEDC [27], the WLTP cycle has been developed. Since WLTP is extracted from in-use driving data, it provides more consistency with real-world fuel consumption and emissions [10,28].…”