Estimation of Fuel Economy on Real-Word Routes for Next-Generation Connected and Automated Hybrid Powertrains The Ohio State University

Format:

Book

Conference/Event

Author/Creator:

Gupta, Shobhit, author.

Contributor:

Aggoune, Karim

Canova, Marcello

Kirwan, John

Olin, Pete

Rajakumar Deshpande, Shreshta

Rizzoni, Giorgio

Tufano, Daniela

Conference Name:

WCX SAE World Congress Experience (2020-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

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 properly benchmark the fuel consumption reduction potential of advanced cylinder deactivation and 48V mild hybridization in the presence of Level 1 connectivity and automation, and including accounting for the variability associated with different routes, traffic scenarios and driver behaviors. An Intelligent Driving system implemented in a demonstration vehicle utilizes advanced route information available from the available V2X communication to determine 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. Numerical results show a 20% average reduction in fuel consumption during several urban driving conditions, compared to a baseline vehicle without cylinder deactivation and CAV features. Of this total efficiency gain, the CAV features deliver 10+% reduction in fuel consumption. Results are shown from both simulation and vehicle testing for a variety of real-world route scenarios

Notes:

Vendor supplied data

Publisher Number:

2020-01-0593

Access Restriction:

Restricted for use by site license