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Research Papers

Comparative Study of Energy Management Strategies for Hydraulic Hybrids

[+] Author and Article Information
Timothy O. Deppen

Department of Mechanical Science
and Engineering,
University of Illinois at Urbana Champaign,
1206 West Green Street,
Urbana, IL 61801
e-mail: tdeppen@illinois.edu

Andrew G. Alleyne

Department of Mechanical Science
and Engineering,
University of Illinois at Urbana Champaign,
1206 West Green Street,
Urbana, IL 61801
e-mail: alleyne@illinois.edu

Jonathan J. Meyer

Department of Mechanical Engineering,
University of Minnesota-Twin Cities,
Minneapolis, MN 55455
e-mail: meyerjo@me.umn.edu

Kim A. Stelson

Department of Mechanical Engineering,
University of Minnesota-Twin Cities,
Minneapolis, MN 55455
e-mail: kstelson@me.umn.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 17, 2012; final manuscript received September 5, 2014; published online November 7, 2014. Assoc. Editor: Nariman Sepehri.

J. Dyn. Sys., Meas., Control 137(4), 041002 (Apr 01, 2015) (11 pages) Paper No: DS-12-1426; doi: 10.1115/1.4028525 History: Received December 17, 2012; Revised September 05, 2014

The sensitivity of energy management strategies (EMS) with respect to variations in drive cycle and system parameters is considered. The design of three strategies is presented: rule-based, stochastic dynamic programming (SDP), and model predictive control (MPC). Each strategy is applied to a series hydraulic hybrid powertrain and validated experimentally using a hardware-in-the-loop system. A full factorial design of experiments (DOE) is conducted to evaluate the performance of these controllers under different urban and highway drive cycles as well as with enforced modeling errors. Through this study, it is observed that each EMS design method represents a different level of tradeoff between optimality and robustness based on how much knowledge of the system is assumed. This tradeoff is quantified by analyzing the standard deviation of system specific fuel consumption (SSFC) and root mean square (RMS) tracking error over the different simulation cases. This insight can then be used to motivate the choice of which control strategy to use based on the application. For example, a city bus travels a repeated route and that knowledge can be leveraged in the EMS design to improve performance. Through this study, it is demonstrated that there is not one EMS design method which is best suited for all applications but rather the underlying assumptions of the system and drive cycle must be carefully considered so that the most appropriate design method is chosen.

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References

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ADVISOR 2004, 2004, AVL, www.avl.com

Figures

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Fig. 1

Series hydraulic hybrid powertrain

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Fig. 2

AEVPS at the University of Illinois at Urbana Champaign

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Fig. 3

Comparison of final rule-based strategy and dynamic programming results over UDDS

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Fig. 4

Combined drive cycle to determine transition probabilities

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Fig. 5

Transition probability map for a certain vehicle speed

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Fig. 6

Efficiency map of the variable displacement pump for a fixed flow rate [16]

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Fig. 7

Switching logic, engine torque, and pressure threshold detection

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Fig. 8

Switching logic, mode selection

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Fig. 9

Comparison of simulated and experimental SHHV states for the rule-based strategy

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Fig. 10

Comparison of simulated and experimental SHHV inputs for the rule-based strategy

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Fig. 11

Comparison of simulated and experimental engine response for the rule-based strategy, markers denote engine operating point

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Fig. 12

Comparison of simulated and experimental SHHV states for the SDP strategy

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Fig. 13

Comparison of simulated and experimental SHHV inputs for the SDP strategy

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Fig. 14

Comparison of simulated and experimental engine response for the SDP strategy, markers denote engine operating point

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Fig. 15

Comparison of simulated and experimental SHHV states for the MPC strategy

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Fig. 16

Comparison of simulated and experimental SHHV inputs for the MPC strategy

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Fig. 17

Comparison of simulated and experimental engine response for the SDP strategy, markers denote engine operating point

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Fig. 18

Urban drive cycle generated from UDDS transition probability map

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Fig. 19

Histogram of SSFC results for all 1000 urban simulation cases

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Fig. 20

Histogram of RMS error results for all 1000 urban simulation cases

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Fig. 21

Histogram of SSFC results for all 1000 highway simulation cases

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Fig. 22

Histogram of RMS error results for all 1000 urban simulation cases

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Fig. 23

Mean SSFC results where error bars are ±1 standard deviation

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Fig. 24

Mean RMS tracking results where error bars are ±1 standard deviation

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