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Research Papers

Adaptive Power-Split Control Design for Marine Hybrid Diesel Powertrain

[+] Author and Article Information
Sergey Samokhin

School of Electrical Engineering,
Aalto University,
Otaniementie 17,
Espoo 02150, Finland
e-mail: sergey.samokhin@aalto.fi

Sotiris Topaloglou

School of Naval Architecture
and Marine Engineering,
National Technical University of Athens,
Zografou Campus,
Athens 15780, Greece
e-mail: akis@lme.ntua.gr

George Papalambrou

Assistant Professor
School of Naval Architecture
and Marine Engineering,
National Technical University of Athens,
Zografou Campus,
Athens 15780, Greece
e-mail: george.papalambrou@lme.ntua.gr

Kai Zenger

School of Electrical Engineering,
Aalto University,
Otaniementie 17,
Espoo 02150, Finland
e-mail: kai.zenger@aalto.fi

Nikolaos Kyrtatos

Professor
School of Naval Architecture
and Marine Engineering,
National Technical University of Athens,
Zografou Campus,
Athens 15780, Greece
e-mail: nkyrt@lme.ntua.gr

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 12, 2016; final manuscript received September 19, 2016; published online November 14, 2016. Assoc. Editor: Ardalan Vahidi.

J. Dyn. Sys., Meas., Control 139(2), 021012 (Nov 14, 2016) (11 pages) Paper No: DS-16-1085; doi: 10.1115/1.4034804 History: Received February 12, 2016; Revised September 19, 2016

It is known that mechanical wear and tear of components of large marine engines throughout their lifetime can cause the engine dynamics to alter. Since traditional control systems with fixed parameters cannot deal with this issue, the engine performance may degrade. In this work, we introduce adaptive control algorithms capable of adapting the control system in order to preserve the engine performance once its dynamics deviate from the nominal ones. Particularly, the direct and indirect model reference adaptation mechanisms are studied. In this work, the case of degraded oxygen sensor is investigated as an example of engine components deterioration throughout its lifetime. The controllers are implemented in Simulink, and their performance is evaluated under both nominal and degraded sensor conditions. Specifically, the sensor degradation is imitated by altering its time-delay. In such conditions, adaptive controllers demonstrate a notable improvement in tracking performance compared to the fixed parameters proportional-integral (PI) controller. Finally, the designed controllers are validated on the hybrid marine engine testbed using dSpace rapid prototyping system.

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References

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Figures

Grahic Jump Location
Fig. 1

Lambda control diagram of the hybrid integrated propulsion powertrain. C, compressor and T, Turbine. The lambda sensor placement is shown schematically (located downstream the turbine in real setup).

Grahic Jump Location
Fig. 2

Model parameter estimation. Measured and simulated data for the engine speed and lambda. Legend: —— simulated, - - - measured.

Grahic Jump Location
Fig. 3

Model parameter estimation. Measured and simulated data for the engine torque. Water-brake and induction motor torque profiles as well as the PRBS excitation signal for frequency inverter are shown.

Grahic Jump Location
Fig. 4

Model validation with the mild propeller curve. Measured and simulated data for the engine torque and lambda. Legend: —— simulated, - - - measured.

Grahic Jump Location
Fig. 5

Model validation with the mild propeller curve. Measured and simulated data for the engine torque. Water-brake and induction motor torque profiles as well as the commanded signal for the frequency inverter are also shown.

Grahic Jump Location
Fig. 6

Direct adaptive control algorithm block-diagram. Designations: Nesp, engine speed set-point, Mesp, load torque set-point.

Grahic Jump Location
Fig. 7

Indirect adaptive control algorithm block-diagram. Designations: Nesp, engine speed set-point, Mesp, load torque set-point.

Grahic Jump Location
Fig. 8

Simulation study of the adaptive controllers. Lambda set-point tracking λ = 3.1 during load step-change. Nominal and measurement delay cases are compared. Load step change 500 → 300 (N·m) is done at 250 s and 300 → 500 (N·m) at 265 s. Legend: —— nominal case, -- measurement delay, - - - lambda set-point.

Grahic Jump Location
Fig. 9

Hybrid-integrated propulsion powertrain on which all the measurements and experiments presented in the paper are performed

Grahic Jump Location
Fig. 10

Experimental verification of the adaptive controllers. Lambda set-point tracking λ = 3.1 during mild load step-change 400 → 300 → 400 (N·m). Nominal and measurement delay cases are compared. Load step change 400 → 300 (N·m) is done at 80 s and 300 → 400 (N·m) at 95 s. Legend: —— nominal case, -- measurement delay, ⋅⋅⋅ load, - - - lambda set-point.

Grahic Jump Location
Fig. 11

Experimental verification of the adaptive controllers. Lambda set-point tracking λ = 3.1 during aggressive load step-change 500 → 300 → 500 (N·m). Nominal and measurement delay cases are compared. Load step change 500 → 300 (N·m) is done at 170 s and 300 → 500 (N·m) at 185 s. Legend: —— nominal case, -- measurement delay, ⋅⋅⋅ load, - - - lambda set-point.

Grahic Jump Location
Fig. 12

Experimental results. Lambda online identification. Lambda set-point λ = 3.1 is also shown. Legend: —— measured lambda, - - - online identified lambda.

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Fig. 13

Experimental verification of the adaptive controllers. Torque curves for diesel engine and electric motor are shown for the designed controllers during lambda set-point tracking λ = 3.1.

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Fig. 14

Experimental study. Measured NOx emissions with the AFR closed-loop control and degraded UEGO sensor.

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