Estimation of Fuel Economy on Real-World Routes for Next-Generation Connected and Automated Hybrid Powertrains 2020-01-0593
The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.
This paper describes a methodology to benchmark the fuel consumption reduction potential of a Level 1 Connected and Automated Vehicle (CAV) with advanced cylinder deactivation and 48V mild hybridization, in the presence of variability induced by route characteristics, traffic and driver behavior. An Intelligent Driving system utilizes advanced route information available from the navigation system and GPS, as well as a V2X communication module to determine the optimal vehicle velocity and battery state of charge profiles that aim at minimizing fuel consumption along a driver-selected route without sacrificing travel time. Since the presence of traffic and the behavior of different drivers strongly affects the fuel consumption and vehicle travel time, a Monte Carlo simulation is conducted to determine the statistical distribution of the results when introducing variability in the inputs. Fuel efficiency benefits are dependent on the route characteristics, traffic conditions and driver behavior. For the route evaluated in this paper, numerical results show as much as 15% to 19% reduction in fuel consumption, compared to a mild hybrid baseline vehicle without cylinder deactivation and CAV features.
Citation: Gupta, S., Rajakumar Deshpande, S., Tufano, D., Canova, M. et al., "Estimation of Fuel Economy on Real-World Routes for Next-Generation Connected and Automated Hybrid Powertrains," SAE Technical Paper 2020-01-0593, 2020, https://doi.org/10.4271/2020-01-0593. Download Citation
Shobhit Gupta, Shreshta Rajakumar Deshpande, Daniela Tufano, Marcello Canova, Giorgio Rizzoni, Karim Aggoune, Pete Olin, John Kirwan
The Ohio State University, Universita Degli Studi di Napoli, Delphi Technologies, Inc.