1. The document presents fault diagnosis methodologies for the air intake path of automotive engines. 2. Key contributions include developing models to monitor air filter health and estimate unmeasurable parameters using second order sliding modes. 3. Virtual sensors are created for manifold pressure and engine speed, and the methods diagnose intake leaks, sensor faults, and inefficiencies in the throttle body and crankshaft.

1. Fault Diagnosis Methodologies For Automotive Engine Air Intake Path
QADEER AHMED
M.S (Control Systems), 2009. B.Sc (Mechatronics & Control Engg.), 2007.
Supervised by: Dr. Aamer Iqbal Bhatti ——————————————— More information at qadeer62@ieee.org
Journal Publications
3. Q. Ahmed, A.I. Bhatti, Q. Khan and M. Raza. Condition Monitoring of Gasoline Engine Air Intake system using Second Order Sliding Modes. Special Issue: Variable
Structure Systems in Automotive Applications, International Journal of Vehicle Design, 2011.
2. Q. Ahmed, A.I. Bhatti and M. Iqbal. Virtual sensors for automotive engine sensors fault diagnosis in second order sliding modes. IEEE Sensors Journal, Sept. 2011.
1. Q. Ahmed and A.I. Bhatti. Estimating SI engine efficiencies and parameters in second order sliding modes. IEEE Trans. on Industrial Electronics, Oct. 2011.
Air Filter Health Monitoring
The difference in the estimated value of Caf reveals the health of air filter
health. A value of Caf < 0.3 indicates the replacement of air filter.
MAP sensor health monitoring
The above results confirm the accurate and timely diagnosis of malfunction
in manifold air pressure sensor.
Virtual sensors for gasoline engine
The mentioned results depict that the proposed virtual sensors provide
accurate measurement of manifold air pressure and engine angular speed.
Sensor Fault DiagnosisEngine Model
˙Pm = A1f (Pm) − A2Pmωeηvol
˙ωe = 1
Je
(B1ηcPm − Tf − Tp − Tl)
where,
A1 = RTm
Vm
AE(α)PaCDCaf γc
A2 = Vd
Vm4π
AE(α) = πD2
4 (1 − cos(α+αcl
αcl
))
γc = 1
(RTa) γ( 2
γ+1)
γ+1
γ−1
f (Pm) = 1 − e(Pm
Pa
−1)
B1 = VdQηth(1−(cγ−1
r )−1
)(c2−γ
r )((cγ−1
r )−1)
4πAFR(γ−1)(cr−1)cvTm
Tp = Vd
4π(Pa − Pm)
Tf = 11.72 + 5.69x10−5
ωe + 2.33x10−14
ω2
e
Estimation Methodology
ExperimentationSignificant Contributions
1. The air filter effects on manifold pressure dynamics are
modeled under air filter discharge coefficient (Caf ).
2. Second order sliding mode observer based estimation of Air
filter discharge coefficient (Caf ), Gasoline engine volumetric
efficiency (ηvol), Gasoline engine combustion efficiency (ηc)
and Frictional torque (Tf ).
3. Development of virtual/soft sensors for manifold pressure and
angular speed measurement.
4. Air filter health monitoring.
5. Diagnosis of intake manifold air leakages & throttle body
efficiency.
6. Health monitoring of crankshaft & intake manifold pressure
sensors.
Experimental Setup
Special Thanks
Muhammad Ali Jinnah Univer-
sity, Islamabad, Pakistan.
ICT R & D Funds, Islamabad,
Pakistan.
Introduction
This research work focuses on the development of fault diagnostic algorithms for a highly
nonlinear air intake system of gasoline automotive engine. This system holds prime
importance, as it ensures desired proportions of air fuel in burning mixture. Any
malfunction in this system will result in poor engine efficiency and harmful exhaust
pollutants. Thus, it‘s condition monitoring becomes mandatory. In this thesis, various
un-measurable but critical parameters has been estimated for diagnostic purpose. These
parameters are estimated using the concepts from second order sliding mode theory. For
each of the component involved in air intake system, fault diagnostic methodology is
formulated and verified on actual Honda City engine. Some of the diagnostic scheme are
discussed here.