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Research Papers

Supervisory Control of Parallel Hybrid Electric Vehicles for Fuel and Emission Reduction

[+] Author and Article Information
Dongsuk Kum1

Department of Mechanical Engineering,  University of Michigan, G041 Lay Automotive Laboratory, Ann Arbor, MI 48109-2133 e-mail: dkum@umich.edu

Huei Peng

Department of Mechanical Engineering,  University of Michigan, G036 Lay Automotive Laboratory, Ann Arbor, MI 48109-2133 e-mail: hpeng@umich.edu

Norman K. Bucknor

Propulsion Systems Research Laboratory,  General Motors R&D Center, Warren, MI 48091 e-mail: norman.k.bucknor@gm.com

1

Corresponding author.

J. Dyn. Sys., Meas., Control 133(6), 061010 (Nov 11, 2011) (10 pages) doi:10.1115/1.4002708 History: Received December 07, 2009; Revised May 19, 2010; Published November 11, 2011; Online November 11, 2011

Past research on hybrid electric vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the light-off of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the dynamic programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy. The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solutions over the entire state space via the comprehensive extraction method. The extracted DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up and low cold-start tailpipe emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and adjust while achieving near-optimal fuel economy and emission performance.

Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Schematic of a pre-transmission parallel HEV powertrain

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Figure 2

Block diagram of the cold-start tailpipe emission model

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Figure 3

Correction factors (hot/cold ratio) of various engine outputs as a function of the coolant temperature

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Figure 4

Schematic of the after-treatment system [34]

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Figure 5

Test-based exhaust gas temperature map at the catalytic converter inlet

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Figure 6

Conversion efficiency map of HC using arctan function

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Figure 7

Comparison of model (solid) versus test data (dashed) weighted emission responses for cold-start FTP urban cycle

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Figure 8

Trade-off between fuel economy and HC over various β

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Figure 9

Simulation results of the DP solution for β=200 on the FTP urban cycle

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Figure 10

State space of the optimal control policy (uk*) showing the comprehensive extraction algorithm with a Tcat sweep

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Figure 11

Extracted optimal engine on/off strategy at Tcat  = 700 K and Tcat  = 420 K

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Figure 12

Extracted optimal shift strategy at Tcat=700  K and Tcat=420  K

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Figure 13

Extracted optimal power-split strategy at Tcat  = 700 K and Tcat  = 420 K

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Figure 14

Flowchart of the cold-start SPC

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Figure 15

Flowchart of the DP-based SPC algorithm

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Figure 16

A sample contour plot of the value function f for Tcool  = 350 K and γ = 8000

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Figure 17

Block diagram of the instantaneous optimization algorithm for the MAP-based SPC (solid line: vector; dashed line: scalar)

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Figure 18

Simulation response comparison of DP versus DP-based SPC for the cold-start FTP-72 cycle

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