0
Research Papers

Dynamic Programming-Informed Equivalent Cost Minimization Control Strategies for Hybrid-Electric Vehicles

[+] Author and Article Information
Dekun Pei

Graduate Research Assistant
e-mail: dekun.pei@gatech.edu

Michael J. Leamy

Associate Professor
e-mail: michael.leamy@me.gatech.edu
George W. Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 3, 2012; final manuscript received June 9, 2013; published online June 27, 2013. Assoc. Editor: Luis Alvarez.

J. Dyn. Sys., Meas., Control 135(5), 051013 (Jun 27, 2013) (12 pages) Paper No: DS-12-1246; doi: 10.1115/1.4024788 History: Received August 03, 2012; Revised June 09, 2013

This paper presents a direct mathematical approach for determining the state of charge (SOC)-dependent equivalent cost factor in hybrid-electric vehicle (HEV) supervisory control problems using globally optimal dynamic programming (DP). It therefore provides a rational basis for designing equivalent cost minimization strategies (ECMS) which achieve near optimal fuel economy (FE). The suggested approach makes use of the Pareto optimality criterion that exists in both ECMS and DP, and as such predicts the optimal equivalence factor for a drive cycle using DP marginal cost. The equivalence factor is then further modified with corrections based on battery SOC, with the aim of making the equivalence factor robust to drive cycle variations. Adaptive logic is also implemented to ensure battery charge sustaining operation at the desired SOC. Simulations performed on parallel and power-split HEV architectures demonstrate the cross-platform applicability of the DP-informed ECMS approach. Fuel economy data resulting from the simulations demonstrate that the robust controller consistently achieves FE within 1% of the global optimum prescribed by DP. Additionally, even when the equivalence factor deviates substantially from the optimal value for a drive cycle, the robust controller can still produce FE within 1–2% of the global optimum. This compares favorably with a traditional ECMS controller based on a constant equivalence factor, which can produce FE 20–30% less than the global optimum under the same conditions. As such, the controller approach detailed should result in ECMS supervisory controllers that can achieve near optimal FE performance, even if component parameters vary from assumed values (e.g., due to manufacturing variation, environmental effects or aging), or actual driving conditions deviate largely from standard drive cycles.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Schematic of a post transmission torque-coupled parallel architecture

Grahic Jump Location
Fig. 2

Schematic of the THS-II power-split HEV architecture

Grahic Jump Location
Fig. 3

Fuel economy convergence with number of state discretization points using the UDDS drive cycle

Grahic Jump Location
Fig. 4

Pareto-optimal operation points for the power-split HEV

Grahic Jump Location
Fig. 5

Comparison of s values from the three presented estimation methods

Grahic Jump Location
Fig. 6

Comparison of DP and ECMS SOC trajectories using the UDDS drive cycle

Grahic Jump Location
Fig. 7

Optimization of s for the UDDS drive cycle

Grahic Jump Location
Fig. 8

SOC trajectory using the UDDS drive cycle with overestimated sref

Grahic Jump Location
Fig. 9

Example curves of s(SOC)

Grahic Jump Location
Fig. 10

Fuel economy contours as a function of sref and a using the UDDS drive cycle

Grahic Jump Location
Fig. 11

SOC trajectories in UDDS drive cycle with a = 100

Grahic Jump Location
Fig. 12

SOC trajectories in three repetitions of the UDDS drive cycle with a = 100 and sref = 275 g/kW h

Grahic Jump Location
Fig. 13

Comparison of s values from different estimation methods for the power-split HEV architecture

Grahic Jump Location
Fig. 14

ECMS optimization of s using the UDDS drive cycle

Grahic Jump Location
Fig. 17

Prius gasoline engine map, reproduced with data from Ref. [18]

Grahic Jump Location
Fig. 16

Small diesel engine map, reproduced with data from Ref. [17]

Grahic Jump Location
Fig. 15

Fuel economy contours as a function of sref and a using the UDDS drive cycle

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