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Intelligent Vehicle Fuel Saving Technologies: Comparing Three Primary Categories of Methods

[+] Author Affiliations
Danielle Fredette, Junbo Jing, Umit Ozguner

The Ohio State University, Columbus, OH

Paper No. DSCC2015-9869, pp. V002T31A003; 8 pages
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME


In recent years, numerous control algorithms for connected and automated vehicles have emerged which focus on modifying driving strategy in order to reduce fuel usage. Referred to as “dynamic eco-driving,” these technologies have realized the possibility for additional fuel savings by utilizing information technologies rather than mechanics. The exact methodologies, however, are diverse. Three primary categories of dynamic eco-driving methodologies are identified and described: 1) ad-hoc methods, designed for the purpose of saving fuel but not considering optimality, 2) classical optimization methods, which use fuel usage modeling to solve an optimal control problem forwards in time, whether numerically or analytically, and 3) optimization by dynamic programming, in which a fuel usage-oriented cost function is minimized but solved backwards in time in discrete steps. Representatives from each of these categories are studied and implemented in simulation for comparison. Advantages and disadvantages of each relative to multiple performance measures are discussed.

Copyright © 2015 by ASME
Topics: Fuels , Vehicles



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