Real-World Robustness for Hybrid Vehicle Optimal Energy Management Strategies Incorporating Drivability Metrics-Reference-Cited by-同舟云学术

Real-World Robustness for Hybrid Vehicle Optimal Energy Management Strategies Incorporating Drivability Metrics

Published:2014-08-08 Issue:6 Volume:136 Page:
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Opila Daniel F.¹,Wang Xiaoyong²,McGee Ryan²,Brent Gillespie R.³,Cook Jeffrey A.⁴,Grizzle J. W.⁵

Affiliation:

1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 e-mail:

2. Research and Advanced Engineering, Ford Motor Company, Dearborn, MI 48120 e-mail:

3. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 e-mail:

4. Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI 48109 e-mail:

5. Electrical Engineering and Computer Science Department, Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 e-mail:

Abstract

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.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/doi/10.1115/1.4027680/6116181/ds_136_06_061011.pdf

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