Daniela Tufano, research associate, PhD student, University of Naples Federico II (Italy)
Marcello Canova, faculty mentor
Recently, Real Driving Emission cycles have been introduced to better represent the behavior of vehicle during real-world operation. However, the huge variability of external factors, such as vehicle payload or driver behavior, make fuel economy evaluation a prohibitively complex challenge. The purpose of this research activity is to identify a methodology to properly estimate the fuel economy along real-word route for Next-Generation Connected and Automated Hybrid Powertrains.
To this aim, this research explores the use of Monte Carlo simulation to investigate the effects of the variability induced by driver behaviour and traffic conditions on the fuel economy over different routes. This approach is generally used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.
The tested vehicle utilizes route information available from a Connected/Automated Vehicles system (CAV) with the aim to minimize the fuel consumption along a prescribed route. Initial results from simulation and vehicle testing indicate a 20% average reduction in fuel consumption when compared to a baseline vehicle. Consequently, the results demonstrate the potential benefit available from integrating information from CAV into optimization methods.