MMAE Seminar: Towards 30% Energy Reduction in Passenger Vehicles Via Advanced Integration of Powertrain Control, Connectivity and Automation

The Department of Mechanical, Materials, and Aerospace Engineering will welcome Marcello Canova, professor in the Department of Mechanical and Aerospace Engineering at Ohio State University, for a lecture titled “Toward 30 Percent Energy Reduction in Passenger Vehicles Via Advanced Integration of Powertrain Control, Connectivity, and Automation” on Wednesday, November 9, from 3:30–4:30 p.m. in Room 104 of the John T. Rettaliata Engineering Center. 


Over the past few years we have observed an increase in the integration of connectivity and automation features in both passenger and commercial vehicles. Currently, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies are primarily implemented as driver assistance systems with the aim of increasing safety and driver comfort. However, onboard sensing and external connectivity allows a vehicle to “know” its future operating environment with some degree of certainty, greatly narrowing previous information gaps. These technologies could operate the vehicle and its powertrain more intelligently, with significant energy savings. In 2016 the Advanced Research Projects Agency–Energy (ARPA-E) launched the Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) program, with the goal of demonstrating a 20 percent fuel economy improvement in real-world driving conditions via the use connectivity and level one automation to co-optimize vehicle dynamics and powertrain controls. The program was extended in 2021 to demonstrate 30 percent efficiency improvement on vehicles upfitted with level four automation. This seminar will outline the main research challenges as we integrate advanced vehicle dynamics and powertrain control algorithms with connectivity and automation toward level three automation and beyond. The use of cloud-based route optimization, coupled with adaptation to local traffic conditions via machine learning algorithms, permits the development of hierarchical control approaches and coordinated distributed optimal control algorithms to achieve near-optimal fuel economy. The ability to realize such capabilities in production vehicles is around the corner, and the control community is playing a key role in shaping the future of personal and commercial mobility.


Marcello Canova is a professor in the Department of Mechanical Engineering and the associate director of the Center for Automotive Research at Ohio State University. Canova conducts research in the broad areas of powertrain electrification and transportation systems, with emphasis on modeling, optimization, and associated control problems. His research has been funded by, among others, Ford, General Motors, Stellantis, Honda, the National Science Foundation, the United States Department of Energy and ARPA-E. Canova is a 2016 NSF CAREER Award recipient, and he has earned the Kappa Delta Distinguished Faculty Award (2011), the SAE Vincent Bendix Automotive Electronics Engineering Award (2011), the Lumley Interdisciplinary Research Award (2012, 2020), the SAE Ralph Teetor Educational Award (2016), and the Michael J. Moran Award for Excellence in Teaching (2017). He has also published more than 120 articles in journals and conference proceedings.