0
Research Papers

Optimal Energy Use in a Light Weight Hydraulic Hybrid Passenger Vehicle

[+] 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 61801tdeppen@illinois.edu

Andrew G. Alleyne

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

Kim A. Stelson

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

Jonathan J. Meyer

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

J. Dyn. Sys., Meas., Control 134(4), 041009 (Apr 27, 2012) (11 pages) doi:10.1115/1.4006082 History: Received September 05, 2011; Revised April 08, 2012; Published April 26, 2012; Online April 27, 2012

In this study we present a procedure for the design and implementation of a control strategy to optimize energy use within a light weight hydraulic hybrid passenger vehicle. The hydraulic hybrid utilizes a high pressure accumulator for energy storage which has superior power density than conventional battery technology. This makes fluid power attractive for urban driving applications in which there are frequent starts and stops and large startup power demands. A dynamic model of a series hydraulic hybrid powertrain is presented along with the design of a model predictive control based energy management strategy. Model predictive control was chosen for this study because it uses no future information about the drive cycle in its design. This increases the flexibility of the controller allowing it to be directly applied to a variety of drive cycles. Using the model predictive framework, a holistic view of the powertrain was taken in the design of the control strategy, and the impact of each actuator’s efficiency on overall efficiency was evaluated. A hardware-in-the-loop experiment using an electro-hydraulic powertrain testbed was then used to validate the dynamic model and control performance. Through a simulation study in which each actuator’s efficiency was given varying levels of priority in the objective function, it was found that overall system efficiency could be improved by allowing for small sacrifices in individual component performance. In fact, the conventional wisdom of using the additional degrees of freedom within a hybrid powertrain to optimize engine efficiency was found to yield the lowest overall powertrain efficiency. In this work we present a rigorous framework for the design of an energy management strategy. The design method improves the powertrain’s operational efficiency by finding the best balance between optimizing individual component efficiencies. Furthermore, since the design of the control strategy is built upon an analysis of individual components, it can be readily extended to other architectures employing different actuators.

Copyright © 2012 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 11

Efficiency map of variable displacement pump for fixed flow rate [19]

Grahic Jump Location
Figure 3

AEVPS at the University of Illinois at Urbana-Champaign

Grahic Jump Location
Figure 4

Schematic of the AEVPS powertrain

Grahic Jump Location
Figure 5

Diesel engine efficiency map

Grahic Jump Location
Figure 6

Curve fit for valve flow gain

Grahic Jump Location
Figure 7

Driving load model implementation

Grahic Jump Location
Figure 8

Comparison between AEVPS with load emulation and model for step changes in each actuator command

Grahic Jump Location
Figure 9

Schematic of MPC with supervisory logic

Grahic Jump Location
Figure 10

Comparison between AEVPS and nonlinear/linear models for step changes in each actuator command. The operating point for the linear model was ne,o  = 145 rad/s, Pu,o  = 11.8 MPa, Pd,o  = 10.65 MPa, nm,o  = 75 rad/s, Te,o  = 78.2 Nm, αo  = 0.0785 rad, and uv,o  = 3.5 V.

Grahic Jump Location
Figure 12

Switching logic, engine torque/pressure threshold detection

Grahic Jump Location
Figure 1

United States oil consumption by sector, 2009 [2]

Grahic Jump Location
Figure 2

Schematic of a series hydraulic hybrid powertrain

Grahic Jump Location
Figure 16

Simulated AEVPS engine response for λ2  = 0, λ3  = 0.1, λ4  = 0.9, and dwell time of 10 s; black dots indicate engine operating point and color bar indicates percent of maximum engine efficiency

Grahic Jump Location
Figure 17

Simulated AEVPS engine response for λ2  = 1, λ3  = 0, λ4  = 0, and dwell time of 10 s; black dot indicates engine operating point and color bar indicates percent of maximum engine efficiency

Grahic Jump Location
Figure 13

Switching logic, mode selection

Grahic Jump Location
Figure 14

Efficiency term weighting sweep for dwell time of 10 s, max fuel consumption: 1.5 kg, min fuel consumption: 1.1 Kg

Grahic Jump Location
Figure 15

Simulated AEVPS powertrain response for λ2  = 0, λ3  = 0.1, and λ4  = 0.9, and dwell time of 10 s

Grahic Jump Location
Figure 18

Simulated fuel consumption values for λ2  = 0, λ3  = 0.1, λ4  = 0.9

Grahic Jump Location
Figure 19

AEVPS powertrain response for λ2  = 0, λ3  = 0.1, and λ4  = 0.9, and dwell time of 10 s

Grahic Jump Location
Figure 20

AEVPS engine response for λ2  = 0, λ3  = 0.1, λ4  = 0.9, and dwell time of 10 s; black dots indicate engine operating point and color bar indicates percent of maximum engine efficiency

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In