Hybrid vehicle fuel economy and drive quality are coupled through the “energy management” controller that regulates power flow among the various energy sources and sinks. This paper studies energy management controllers designed using shortest path stochastic dynamic programming (SP-SDP), a stochastic optimal control design method which can respect constraints on drivetrain activity while minimizing fuel consumption for an assumed distribution of driver power demand. The performance of SP-SDP controllers is evaluated through simulation on large numbers of real-world drive cycles and compared to a baseline industrial controller provided by a major auto manufacturer. On real-world driving data, the SP-SDP-based controllers yield 10% better fuel economy than the baseline industrial controller, for the same engine and gear activity. The SP-SDP controllers are further evaluated for robustness to the drive cycle statistics used in their design. Simplified drivability metrics introduced in previous work are validated on large real-world data sets.
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November 2014
Research-Article
Real-World Robustness for Hybrid Vehicle Optimal Energy Management Strategies Incorporating Drivability Metrics
Daniel F. Opila,
Daniel F. Opila
Department of Mechanical Engineering,
e-mail: dopila@andrew.cmu.edu
Carnegie Mellon University
,Pittsburgh, PA 15213
e-mail: dopila@andrew.cmu.edu
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R. Brent Gillespie,
R. Brent Gillespie
Department of Mechanical Engineering,
e-mail: brentg@umich.edu
University of Michigan
,Ann Arbor, MI 48109
e-mail: brentg@umich.edu
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Jeffrey A. Cook,
Jeffrey A. Cook
Electrical Engineering and
Computer Science Department,
e-mail: jeffcook@umich.edu
Computer Science Department,
University of Michigan
,Ann Arbor, MI 48109
e-mail: jeffcook@umich.edu
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J. W. Grizzle
J. W. Grizzle
Electrical Engineering and
Computer Science Department,
Department of Mechanical Engineering,
e-mail: grizzle@umich.edu
Computer Science Department,
Department of Mechanical Engineering,
University of Michigan
,Ann Arbor, MI 48109
e-mail: grizzle@umich.edu
Search for other works by this author on:
Daniel F. Opila
Department of Mechanical Engineering,
e-mail: dopila@andrew.cmu.edu
Carnegie Mellon University
,Pittsburgh, PA 15213
e-mail: dopila@andrew.cmu.edu
Xiaoyong Wang
Ryan McGee
R. Brent Gillespie
Department of Mechanical Engineering,
e-mail: brentg@umich.edu
University of Michigan
,Ann Arbor, MI 48109
e-mail: brentg@umich.edu
Jeffrey A. Cook
Electrical Engineering and
Computer Science Department,
e-mail: jeffcook@umich.edu
Computer Science Department,
University of Michigan
,Ann Arbor, MI 48109
e-mail: jeffcook@umich.edu
J. W. Grizzle
Electrical Engineering and
Computer Science Department,
Department of Mechanical Engineering,
e-mail: grizzle@umich.edu
Computer Science Department,
Department of Mechanical Engineering,
University of Michigan
,Ann Arbor, MI 48109
e-mail: grizzle@umich.edu
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 12, 2013; final manuscript received May 3, 2014; published online August 8, 2014. Assoc. Editor: Shankar Coimbatore Subramanian.
J. Dyn. Sys., Meas., Control. Nov 2014, 136(6): 061011 (10 pages)
Published Online: August 8, 2014
Article history
Received:
July 12, 2013
Revision Received:
May 3, 2014
Citation
Opila, D. F., Wang, X., McGee, R., Brent Gillespie, R., Cook, J. A., and Grizzle, J. W. (August 8, 2014). "Real-World Robustness for Hybrid Vehicle Optimal Energy Management Strategies Incorporating Drivability Metrics." ASME. J. Dyn. Sys., Meas., Control. November 2014; 136(6): 061011. https://doi.org/10.1115/1.4027680
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