Research Papers: Internal Combustion Engines

Model Predictive Engine Speed Control for Transmissions With Dog Clutches

[+] Author and Article Information
Qilun Zhu

Automotive Engineering Department,
Clemson University,
Greenville, SC 29607
e-mail: qilun@g.clemson.edu

Robert Prucka

Automotive Engineering Department,
Clemson University,
Greenville, SC 29607

Michael Prucka

Auburn Hills, MI 48326
e-mail: michael.prucka@fcagroup.com

Hussein Dourra

Auburn Hills, MI 48326
e-mail: hussein.dourra@fcagroup.com

Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 28, 2017; final manuscript received April 25, 2017; published online June 6, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(11), 112803 (Jun 06, 2017) (11 pages) Paper No: GTP-17-1082; doi: 10.1115/1.4036622 History: Received February 28, 2017; Revised April 25, 2017

The need for cost-effective fuel economy improvements has driven the introduction of automatic transmissions with an increasing number of gear ratios. Incorporation of interlocking dog clutches in these transmissions decreases package space and increases efficiency, as compared to conventional dry or wet clutches. Unlike friction-based clutches, interlocking dog clutches require very precise rotational speed matching prior to engagement. Precise engine speed control is, therefore, critical to maintaining high shift quality. This research focuses on controlling the engine speed during a gearshift period by manipulating throttle position and combustion phasing. Model predictive control (MPC) is advantageous in this application since the speed profile of a future prediction horizon is known with relatively high confidence. The MPC can find the optimal control actions to achieve the designated speed target without invoking unnecessary actuator manipulation and violating hardware and combustion constraints. This research utilizes linear parameter varying (LPV) MPC to control the engine speed during the gearshift period. Combustion stability constraints are considered with a control-oriented covariance of indicated mean effective pressure model (COV of IMEP). The proposed MPC engine speed controller is validated with a high-fidelity zero-dimensional engine model with crank angle resolution. Four case studies, based on simulation, investigate the impact of different MPC design parameters. They also demonstrate that the proposed MPC engine controller successfully achieves the speed reference tracking objective while considering combustion variation constraints.

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Fig. 1

Validation of engine model torque output with experimental data

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Fig. 2

Block diagram of the COV of IMEP model

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Fig. 3

Validation of the COV of IMEP model

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Fig. 4

Artificial neural network correction of the air mass flow model is used to avoid numerical solver issues and reduce computational load

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Fig. 5

Results of the proposed MPC engine speed controller tracking a random step change in speed reference

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Fig. 6

Performance for a step increase in speed reference (zoom-in at 0–20th engine cycle of Fig. 5)

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Fig. 7

Performance for a step decrease in speed reference (zoom-in at 100–120th engine cycle of Fig. 5)

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Fig. 8

Comparison between MPCs with different preview horizons

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Fig. 9

Comparison between MPCs with different performance weighting

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Fig. 10

Results of MPC engine speed controller with terminal manifold pressure penalty



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