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Research Papers

Hybrid State Space Modeling of a Spark Ignition Engine for Online Fault Diagnosis

[+] Author and Article Information
E. P. Nadeer

Advanced Technology Development
Centre (ATDC),
Indian Institute of Technology,
Kharagpur 721302, West Bengal, India
e-mail: epnadeer@iitkgp.ac.in

Amit Patra

Professor
Department of Electrical Engineering,
Indian Institute of Technology,
Kharagpur 721302, West Bengal, India
e-mail: amit@ee.iitkgp.ernet.in

Siddhartha Mukhopadhyay

Professor
Department of Electrical Engineering,
Indian Institute of Technology,
Kharagpur 721302, West Bengal, India
e-mail: smukh@ee.iitkgp.ernet.in

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 21, 2016; final manuscript received September 30, 2017; published online December 12, 2017. Assoc. Editor: Junmin Wang.

J. Dyn. Sys., Meas., Control 140(4), 041010 (Dec 12, 2017) (14 pages) Paper No: DS-16-1152; doi: 10.1115/1.4038164 History: Received March 21, 2016; Revised September 30, 2017

In this work, a nonlinear hybrid state space model of a complete spark ignition (SI) gasoline engine system from throttle to muffler is developed using the mass and energy balance equations. It provides within-cycle dynamics of all the engine variables such as temperature, pressure, and mass of individual gas species in the intake manifold (IM), cylinder, and exhaust manifold (EM). The inputs to the model are the same as that commonly exercised by the engine control unit (ECU), and its outputs correspond to available engine sensors. It uses generally known engine parameters, does not require extensive engine maps found in mean value models (MVMs), and requires minimal experimentation for tuning. It is demonstrated that the model is able to capture a variety of engine faults by suitable parameterization. The state space modeling is parsimonious in having the minimum number of integrators in the model by appropriate choice of state. It leads to great computational efficiency due to the possibility of deriving the Jacobian expressions analytically in applications such as on-board state estimation. The model was validated both with data from an industry standard engine simulation and those from an actual engine after relevant modifications. For the test engine, the engine speed and crank angle were extracted from the crank position sensor signal. The model was seen to match the true values of engine variables both in simulation and experiments.

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References

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Figures

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Fig. 1

Fault diagnosis scheme using a within-cycle model

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Fig. 2

Block diagram of the SI engine system

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Fig. 3

Example engine variables for proposed and AMESim models for the entire throttle range

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Fig. 4

Detailed view of some engine variables from the proposed and AMESim models

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Fig. 5

Engine variables under IM leak fault

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Fig. 6

Engine variables under EM leak fault

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Fig. 7

Engine variables under intake valve leak

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Fig. 8

Engine variables under exhaust valve leak

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Fig. 9

Engine variables under injector fault in cylinder 1

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Fig. 10

Extracted throttle area and engine speed signals

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Fig. 11

Engine variables from the developed model (Simulink) and real car

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