Engine plant model_development for HIL system and application to on-board diagnostic verification

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2011-01-0457
Engine Plant Model Development for HIL System and Application to
On-Board Diagnostic Verification
Yixin Chen, Jeffrey Pfeiffer, Ken Simpson
Delphi Powertrain Systems
Copyright © 2011 SAE International
ABSTRACT
This paper first presents a basic mean value engine plant model implemented in a hardware-in-the-loop (HIL) system. The plant model
includes some basic engine parameters such as engine speed, manifold absolute pressure, etc., which are critical to both control
algorithm integrity and default actions that result from improper signal performance (e.g., ECU shuts down due to corrupted signal(s)).
The model is then improved to develop the HIL bench-based testing capabilities in the areas where a vehicle has traditionally been
required. The on-board diagnostic monitor tests covered by SID $06 of SAE J1979 are selected as a case study. Specifically, for OBD
exhaust gas sensor monitor testing purposes, the oxygen sensor model is developed to simulate normal or abnormal binary switching
signals which might have asymmetric “lean to rich” and “rich to lean” transitions, or largely off maximum/minimum sensor voltages,
etc. For a variable valve timing (VVT) system, a cam phaser model is developed which enables the HIL system to simulate normal
cam phasing as well as phasing system errors such as slow response, cam signal stuck, etc. Verification of catalyst monitoring is made
possible through integration of the ASAP3 protocol into the model to accomplish the capability for the testing script to accurately
synchronize the front and post oxygen sensor signals with the ECU’s air/fuel ratio command. The basic ideas behind the models will
be explained in the paper. With the HIL-based test bench running the newly-developed engine plant model, we have developed the
capability to automatically conduct OBD exhaust gas, VVT, and catalyst monitor tests, which are normally done in vehicle. Some test
results will be demonstrated in the paper as well.
INTRODUCTION
Testing automotive ECU software is important to ensure product quality. Besides the tests which are conducted on a static bench,
which normally provide the ECU the un-related vehicle sensor input signals only, there are many tests which have traditionally been
executed in a vehicle, with all sensor inputs and ECU outputs coordinately changing. Conducting such tests on a static bench becomes
quite a challenge because the ECU may fail many on-board rationality diagnostics. To decrease the testing costs associated with using
engineering vehicles and to improve testing repeatability, hardware-in-the-loop (HIL) based testing should be used to run many of the
vehicle tests.
There are two types of test methods while using an HIL-based testing system: open loop and closed loop. In open loop testing, the HIL
testing system is used in the same way as a static bench, where sensor inputs do not coordinately change. Therefore, the open loop
based HIL testing system is not very suitable for running vehicle tests, although it might be able to run some limited vehicle tests if
using scripts to simulate some vehicle system dynamics. Such open loop testing has the advantage of repeatability, and shorter testing
time compared with static bench test. In closed loop testing, the HIL simulator is running a vehicle system or sub-system model in the
background, which responds to the ECU outputs and provides the dynamically-changing sensor inputs to the ECU, and therefore is
suitable to perform tests that were previously limited to be performed on a vehicle.
To develop HIL-based closed loop testing, a vehicle or sub-system plant model has to be developed first. For ECU software
verification purposes, it is not necessary to have a very high fidelity vehicle plant model, but the plant model should be good enough
to keep the ECU from failing critical diagnostics which can cause the ECU to not run the specific function to be tested. For example,
in testing fuel closed loop control, it is required that the plant model at least simulate reasonable engine speed, MAP, exhaust oxygen,
and coolant temperature signals so the ECU will enter closed loop control mode first. In addition, the HIL interface should be

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designed in a flexible way so the final sensor signals can come from either model calculations or user inputs. Allowing user inputs can
guarantee the necessary algorithm enabling conditions and therefore simplifies the test design in many cases.
Based on the idea described above, we have integrated a mean value vehicle plant model to the HIL based ECU software testing
system. Our application focuses on engine control algorithm verification. With the plant model, the HIL system is able to provide the
ECU with basic engine dynamic sensor signals, keeping the ECU in the normal engine control mode, such as closed loop fuel control
or wide open throttle (WOT), without disabling many diagnostics.
In the following sections, first, we present the basic structure of the mean value vehicle plant model. Then, we present the plant model
improvement, which was motivated by building our HIL-based bench testing capability for tests which are normally performed in
vehicle. The on-board diagnostic tests defined by SID $06 of SAE1979 are selected as a case study. Specifically, the newly-
developed oxygen sensor and cam phaser models are explained, and testing results obtained using the models on the associated
diagnostic tests are demonstrated for SID $06 compliance.
MEAN VALUE ENGINE PLANT MODEL
This section presents a mean value engine plant model which is integrated to a HIL testing system for the purpose of engine
management system software verification.
ENGINE SPEED – Engine speed represents the engine crankshaft rotational speed. The crankshaft rotation is caused by torque from
either the starter motor or combustion in the engine cylinders. The engine crankshaft rotational dynamics [1], [2] can be represented
by:
floadstarterindee TTTTI −−+=ω& (1)
where
eω engine speed (rads/sec);
eI engine rotational moment of inertia which is a constant given same type of engine (Kg m2
);
indT the indicated combustion torque (Nm);
starterT the starter torque which is used to simulate engine start only (Nm);
loadT the external load torque (Nm);
fT the engine pumping and friction losses (Nm).
indT is generated by fuel combustion and can be represented by [1]:
e
fiu
ind
mH
T
ω
η &
= (2)
where
uH fuel heating value (kJ/kg) [3];
iη thermal efficiency;
fm& fuel mass flow rate (g/s).
Every two engine revolutions, fuel is injected into the cylinder. Fuel mass flow rate into cylinder fm& is dependent on fuel injector
“ON” time, fuel flow rate in the fuel rail, and engine speed, and can be derived as below:

3. Page 3 of 19
eFuelRailf Tcm ωγ **∗=& (3)
where
T average fuel injector “ON” time;
FuelRailγ fuel flow rate in fuel rail;
c constant.
fT consists of two parts: friction losses which are proportional to engine speed; and pumping losses which are proportional to engine
speed and the pressure difference between exhaust and manifold. Since exhaust pressure is constantly equal to the atmospheric
pressure, fT can be expressed as polynomials of engine speed and manifold pressure as follows [2]:
manmapeeef pbpbaaaT 1021
2
0 +•+++= ωωω (4)
where manp is intake manifold pressure.
loadT is typically provided by a torque converter which couples the engine to the transmission. A simple model is shown in [4] as
below :
3
eload cT ω= (5)
where c is load factor.
INTAKE MANIFOLD PRESSURE – By the ideal gas law, intake manifold pressure can be expressed as:
man
manman
man
V
RTm
p = (6)
where
manp intake manifold pressure (pascal)
R gas constant for mixture in intake manifold (= 287 J Kg-1
K-1
) [5];
manm air mass in intake manifold (Kg);
manT intake manifold temperature (Kelvin);
manV is the intake manifold volume (m3
).
By taking derivatives on Eq. (6), we have
)( aoai
man
man
man
man
man
man mm
V
RT
m
V
RT
p &&&& −== (7)
where aim& and aom& represent the air mass flow rate in and out of intake manifold in unit of kg/s, respectively.
The final intake manifold pressure manp can be solved by the integration of Eq. (7) if aim& and aom& are solved.

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The air mass flow out of intake manifold (or into the combustion cylinders) can be derived by using the speed density formula and
ideal gas law as below [1], [2]:
man
man
dvol
man
man
d
e
vol
man
d
e
vold
e
volao
RT
p
V
n
RT
p
V
m
VVm
1204
V44 man
η
π
ω
η
π
ω
ηρ
π
ω
η
=
===&
(8)
where
n engine speed in RPM;
volη volumetric efficiency (VE);
dV total engine cylinder displacement (m3
);
ρ air density in intake manifold (kg/m3
).
MASS AIR FLOW – Mass air flow is the term aim& in Eq. (7). For a throttle body with an idle bypass valve, aim& consists of two
parts: air flow from the idle bypass valve and the throttle plate opening. Most modern vehicles use electronic throttle control (ETC) for
all air control including idle air, and therefore we only discuss the air flow from the throttle opening in this paper.
The mass air flow can be calculated as a product of three variables as below [2]:
PRITCMAXmai ••= )(α& (9)
where MAX is the maximum possible intake air flow rate, which is a constant dependant on the size of the throttle body; )(αTC is
the air flow correction term by throttle open angle α ; PRI is the air flow correction term by the pressure ratio influence function.
The maximum possible intake air flow is the choked or critical flow ([6], Appendix C) and therefore we have:
( ) t
man
baro A
RT
MAX
p
•





+
=
−∗
+
)1(2
1
2/1
1
2 γ
γ
γ
γ (10)
where pbaro
is barometric pressure (pascals); γ is the ratio of specific heats (= 1.4); tA is throttle cross-sectional area, which is
2
4
D
π
, where D is the throttle diameter.
)(αTC is calculated as:
)cos(1)( 0ααα +−=TC (11)
where 0α is the minimum throttle angle which is the throttle angle when the throttle plate is commanded to close. )(αTC has a
minimum value when 0=α (throttle closed) and maximum value when 2/0 παα =+ (wide open throttle).
PRI is the pressure ratio influence function, which describes the choked/sonic flow that occurs through the throttle valve [1], [2], [6].
PRI is calculated as:

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




≤
>





−
−
−=
(sonic)1
(choked)
1
1
2
cr
cr
c
cr
pp
pp
p
pp
PRI (12)
where ambmanr ppp /= , which is less or equal to 1 (as intake manifold pressure is less or equal to atmospheric pressure). cp is
called the critical pressure ratio which is approximately 0.5283.
COOLANT TEMPERATURE – Coolant temperature depends on the total heat transferred to the coolant, the coolant mass, and the
coolant specific heat capacity [7]. Coolant temperature can be predicted by:
dt
HM
PHP
TT
t
coolantcoolant
lostinin
coolantcoolant ∫ •
−+
+=
0
)0( (13)
where
)0(coolantT initial coolant temperature;
inP combustion power transferred into coolant (J/S);
inH heat transfer term caused by difference between engine oil and coolant temperatures (J/S);
lostP heat loss due to air from fan, moving vehicle (vehicle speed), and difference between ambient and coolant temperatures (J/S);
coolantM coolant mass (kg);
coolantH coolant specific heat capacity (J/(kg.K)).
ENGINE OIL TEMPERATURE – Engine oil temperature can be modeled the same way as the coolant temperature. Refer to Eq. (13),
for engine oil temperature modeling, all parameters should be engine oil temperature related. For example, M should be engine oil
mass, and H engine oil specific heat capacity.
VEHICLE SPEED – Given the force F and vehicle weight m , the acceleration a can be simply solved by Newton second law.
Then, vehicle speed v can be derived by integration as below:
∫∫ =∗=
tt
dt
m
F
dtav
00
(14)
The force F consists of three parts: tire force tireF which is related to the transmission output shaft; load loadF which is related to
the wind drag force (proportional to the square of vehicle speed); brake force, and road grade; and tire friction frictionF which
increases when wheel speed increases, and has the following relation:
frictionloadtire FFFF −−= (15)
In a simplified way, we can model the three parts as below:

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2
43
2
2
1
vkvkF
FFvkF
TkF
friction
GradeForceBrakeForceload
rqTransOutTotire
∗+∗=
++∗=
∗=
(16)
OXYGEN SENSOR – Ideal stoichiometric gasoline combustion (burning out all fuel and air) has an air/fuel ratio about 14.7. The
traditional switching oxygen sensor signal has a range of 100 mV – 900 mV, with a low value (100 mV) indicating lean mode (air/fuel
ratio > 14.7, not enough fuel) and a high value (900 mV) indicating rich mode (air/fuel ratio < 14.7, too much fuel). Therefore, air/fuel
ratio can be used to generate the oxygen sensor signal.
Given fuel flow fm& shown in Eq. (3) and air flow aom& shown in Eq. (8) into cylinder, air/fuel ratio can be estimated by:
fao mmFA && // = (17)
The oxygen sensor output can be simulated as:



<
≥
=
7.14/mV,900
7.14/mV,100
2 FA
FA
VO (18)
In addition to above oxygen sensor simulation, driven by OBD-II oxygen sensor monitor test, we have also developed an oxygen
sensor signal generator which can simulate different oxygen sensor failures. The oxygen sensor signal consists of two sinusoidal
driven parts: the first representing the “Lean-to-Rich” portion of the signal, and other representing the “Rich-to-Lean” portion of the
signal. The oxygen sensor signal generator output is defined by:





<<+
−
−
−+
<<+−+
=
NnN
NN
Nn
VVV
Nn
N
n
VVV
V
LHM
LHM
O
α
π
π
α
α
α
π
α
π
,)
2
sin()(5.0
0,)
2
3
sin()(5.0
2
(19)
where )(5.0 LHLM VVVV −+= ; N is number of ticks and represents the period of
2OV ; α is percentage of time “Lean-to-
Rich” versus the period of
2OV . Figure 1 shows three different
2OV waveforms, where (a) is a normal waveform ( N = 1000, LV =
100 mV, HV = 900 mV, α = 0.5); (b) simulates a fault with a too slow “Lean-to-Rich” response (α = 0.8); and (c) simulates a fault
with too slow “Rich-to-Lean” response (α = 0.2) and a too high LV value ( LV = 600 mV).

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(a) Normal O2 signal
(b) “Lean-to-Rich” response is too slow ( 8.0=α )
(c) “Rich-to-Lean” response is too slow ( 2.0=α ) and LV value is too high ( LV = 600 mV)
Figure 1: Three different O2 signals
The simulator user can choose to use either the oxygen sensor signal by Eq. (18) or the oxygen sensor signal generator by Eq. (19).
CAM POSITION – the engine’s camshaft is used to open and close the intake and exhaust valves. Cam phasing is widely used in
modern vehicles to adjust the advance time to open the intake valve and the retard time to close the exhaust valve, and its purpose is to
improve fuel economy and engine performance. Figure 2 shows how cam phasing control works. Given a target cam position, the
ECU commands a PWM signal which is used to control the cam phasing angle through modification of the oil control valve (OCV)

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flow [8]. The difference between the target and actual cam positions is used in a PID control algorithm to adjust the PWM duty cycle
of the control signal so the cam position will stay around the target.
Figure 2: Variable cam phasing block diagram
The cam phaser position is modeled based on the relation between the duty cycle of control signal to the OCV flow. An example an
OCV flow curve is shown in Figure 3. With a 0% duty cycle to the valve, the cam phaser will be driven to the default position
(typically full retard on intake, or full advance on exhaust). A 100% duty cycle command will drive the phaser to its maximum
advanced position for the intake valve and the maximum retarded position for exhaust valve. An intermediate duty cycle, usually
between 35% and 75%, will drive OCV to a choked-flow position, and hold the phaser at the current position.
Figure 3: Example of oil control valve flow characteristic
A low duty cycle signal (such as 0%) will drive OCV flow in the opposite direction compared with the high duty cycle signal (such as
100%). If we define the oil flow for low duty cycles as negative flow and high duty cycles as positive flow, the flow curve can be
approximated using a cubic function, with saturation at the open-loop flow. The final relation from control signal duty cycle x to cam
position y is modeled as:
100%
Oil flow
Open-loop flow
Hold; no flow
Open-loop flow
Duty cycle
0% 100%
Oil flow
Open-loop flow
Hold; no flow
Open-loop flow
Duty cycle
0%

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∫ −=
t
dtkxky
0
3
21 )( (20)
where 1k is phasing response slope and 2k is the duty cycle value at the hold position. Both 1k and 2k are battery voltage and oil
temperature dependent and modeled by a table.
HIL SYSTEM BASED DEVELOPMENT TO SUPPORT OBD MONITOR TESTING
The on-board diagnostic system is capable of diagnosing vehicle sensor failures which could cause dramatic emissions increase and
impact the environment. CARB (California Air Resources Board) and SAE-J1979 documents define the OBD-II requirements OEMs
must meet in order to sell vehicles in North America. Specifically, SAE-J1979 has defined the service ID SID$06 to access the status
of different diagnostic sub-systems such as the exhaust gas sensor, catalyst converter, VVT (variable valve timing), etc., through a
standardized serial communication line. The verification of the SDI$06 monitor tests is a necessary part in achieving and
demonstrating compliance with OBD-II standards.
Most sub-system diagnostic tests included in SID$06 will only run in normal engine working conditions. For example, the exhaust gas
sensor diagnostics only run while the engine is in fuel closed loop control as well as many other conditions, such as engine RPM,
airflow, etc. Therefore, these tests are normally conducted on an actual vehicle, where all sensor data will change accordingly. To
decrease verification cost and increase the verification repeatability, it is desireable to conduct this verification using the HIL system
based testing bench with a reasonable vehicle model running in the background. In the following sections, we will present HIL system
based test development examples for exhaust gas sensor and VVT monitor tests.
SID$06 MONITOR TEST ON EXHAUST GAS SENSOR – Figure 4 shows the standardized test ID value example for which SAE
J1979 has defined for the exhaust gas sensor [9].
Figure 4: Standardized test IDs for exhaust gas sensor
Given the switching oxygen sensor signal, test IDs TID$01-$09 represent different parameters. For example, TID$06 is the elapsed
time while the signal transitions from a lean to rich value and represents the oxygen sensor lean to rich response time. A slow sensor
response time indicates an oxygen sensor failure and should be reported.

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Figure 5: Oxygen sensor response diagnostics flowchart
Figure 5 shows the oxygen sensor response diagnostics flowchart. The algorithm consists of three primary steps: determine if enable
criteria are met; data collection; data analysis and report. To perform an evaluation of the sensor performance, the enable criteria must
be met in step 1, otherwise the algorithm will simply skip steps 2 and 3, which means there will be no diagnostic data generated and
the data provided by SID$06 will be simply be defaulted zero values as required by SAE-J1979. Obviously, this case would not verify
the oxygen sensor response diagnostics algorithm and the data access by SID$06. With the HIL-based testing system running the
engine plant model in the background, it is much easier to pass the first step and therefore make testing on the bench possible.
Figure 6 (a) and (b) show two examples of SID$06/OBDMID$01 (OBD monitor ID for “Exhaust Gas Sensor Monitor Bank 1 –
Sensor 1”) readings corresponding to the normal oxygen sensor input shown in Figure 1(a) and the faulted (slow lean-to-rich
response) waveform shown in Figure 1(b). With the HIL system running the engine plant model in the background, it’s much easier
for the oxygen sensor diagnostics algorithm to pass the enable criteria checking, as fuel is in stoichiometric closed loop control, as
well as many other conditions (Step 1 shown in Figure 5). The steps of collecting and analyzing the diagnostic data may then
continue. In Figure 6(a), the data readings for SID$06/OBDMID$01/TID$05 (rich to lean sensor switch time) is 0x0081 (129 ms for
USID$10: 1 ms/bit) and SID$06/OBDMID$01/TID$06 (lean to rich sensor switch time) is 0x007D (125 ms). This verifies the rich to
lean switch time is about equal to the lean to rich switch time, given a normal oxygen sensor signal. In Figure 6(b),
SID$06/OBDMID$01/TID$05 is 0x0039 (57 ms) and SID$06/OBDMID$01/TID$06 is 0x00CA (202 ms). This verifies the
diagnostics algorithm is able to detect the slow lean-to-rich response which is a required test outcome defined in SAE J1979.

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Rich to lean sensor switch time =
SID$06/OBDMID$01/TID$05 = $0081
129 ms for USID$10 (1 ms/bit)
Lean to rich sensor switch time =
SID$06/OBDMID$01/TID$06 = $007D
125 ms for USID$10 (1 ms/bit)
OR11NRLA: ECU measured rich to lean sensor switch time;
OR11NLRA: ECU measured lean to rich sensor switch time
Normal O2 signal with equal lean-to-rich
and rich-to-lean switch times
(a) SID$06/OBDMID$01 testing data for a normal oxygen sensor waveform

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Faulty O2 signal with longer lean-to-rich
time and shorter rich-to-lean switch time
OR11NRLA: ECU measured rich to lean sensor switch time;
OR11NLRA: ECU measured lean to rich sensor switch time
Lean to rich sensor switch time =
SID$06/OBDMID$01/TID$06 = $00CA
202 ms for USID$10 (1 ms/bit)
Rich to lean sensor switch time =
SID$06/OBDMID$01/TID$05 = $0039
57 ms for USID$10 (1 ms/bit)
(b) SID$06/OBDMID$01 testing data for a fault oxygen sensor waveform with slow lean-to-rich response time
Figure 6
SID$06 MONITOR TEST ON CAM PHASING – SAE J1979 designates OBDMID$35 and $36 to VVT monitoring for banks 1 and 2
[9]. Specifically, CARB regulations [10] require monitoring of the VVT system for target error and slow response malfunctions. For
example, TID$C2 and $C3 are assigned in Delphi engine management software for bank 1 intake phaser steady error and response
time respectively.
Similar to the logic shown in Figure 5, the cam phasing algorithm first checks if all enabling criteria are met. Only after all enabling
conditions are met, the algorithm goes to the next step, monitoring cam phasing control and running the phaser diagnostics. If the error
between the desired and actual cam positions is too big and has been greater than an error threshold for a relatively long time, the
diagnostic algorithm will set the cam phaser steady-state error failure flag to true. TID$C2 data read out through
SID$06/OBDMID$35 should show the phaser target position error information. For cam phaser response, the diagnostic algorithm
dynamically calculates the cam phasing rate of change, in crank-angle degrees per second. If the phasing rate is too small, the
algorithm will indicate a phaser response failure and TID$C3 data will reflect the phaser response rate.

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Figure 7(a) shows the HIL bench based verification of the cam phasing control. With the engine plant model running in the
background, the phasing control algorithm is enabled for the bench condition. The red curve shows the desired bank 1 intake cam
phasing position, the green curve is the actual cam position. The actual cam position follows the desired cam position with a little
overshoot, verifying the CAM phasing control model.
To verify the TID$C2 reading for a steady phaser error, first, the following steps are done to simulate 30 degree phaser error:
• In the engine controller software, the desired cam position is set to 50 degrees using a calibration tool;
• In the HIL system, the manual cam signal phasing control is enabled and the cam phase angle is set to 20 degrees.
Then, with the cam phasing algorithm enabled, the diagnostic indicates a phaser steady error and logs the error. Figure 7(b) shows the
TID$C2 reading through the SID$06/OBDMID$35 message. TID$C2 data is 0x0B9C which is 29.72 degrees with USID$9C
(0.01deg/bit).
Figure 7(c) shows the TID$C3 data reading for cam phaser response rate. A slow phaser response can be simulated using the HIL
system by decreasing the response slope term 1k , therefore taking a longer time for the cam position to approach the desired CAM
position. The TID$C3 reading is 0x312D, which means the time to reach the cam phasing maximum angle (60 crank degrees) is
12589 ms with USID$10 (1ms/bit).
Desired CAM phasing angle
Actual CAM phasing angle
(a) CAM phasing PID control verification

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CAM phaser steady error = ID$06/OBDMID$35/TID$C2 data
= 0x0B9C 29.72 degrees (0.01deg/bit for USID$9C ).
(b) CAM phaser steady error verification by SID$06/OBDMID$35/TID$C2

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CAM phaser response rate = SID$06/OBDMID$35/TID$C3 data
= 0x312D 12589 ms (1ms/bit, USID$10).
(c) CAM phaser slow response diagnostics verification by SID$06/OBDMID$35/TID$C3
Figure 7
SID$06 MONITOR TEST ON CATALYST – CARB requires monitoring of the catalyst system for proper conversion capability for
OBD II compliance [10]. SAE 1979 has assigned OBDMID$21-$22 for accessing the data of the catalyst monitor bank 1-2 [9].
Specifically, Delphi uses oxygen storage capacity (OSC) as the measurement of the catalyst conversion capability [12]. The basic idea
is:
• In engine idle, after passing enabling conditions, the catalyst diagnostics algorithm intentionally controls to fill the catalyst
with a lot of oxygen by commanding fuel “lean”;
• Subsequently, the fuel is commanded “rich”, after which the time for the catalyst to release all oxygen is measured by data
readings from the rear oxygen sensor, which is mounted after catalyst.
It is somewhat challenging to perform the verification of the catalyst diagnostic algorithm on the bench. Even with the HIL-based test
bench running the engine plant model, the plant oxygen sensor model calculates air/fuel ratio based on plant air and fuel models, and
therefore has to be very accurate to get the oxygen sensor signals synchronized with the ECU air/fuel ratio command. To develop the
HIL based bench testing capability for SID$06/OBDMID$21-$22, we have used the ASAP3 protocol [11] which allows the HIL
system to check when the ECU starts running the catalyst diagnostics.

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The scripting flowchart shown in Figure 8 has been implemented for the HIL based testing system as part of the verification for
SID$06/OBDMID$21-$22. The block to check if “Catalyst diagnostics enable conditions met” is done using ASAP3 protocol for HIL
system to read an ECU internal software variable. The rest of the blocks in Figure 8 are purely controlling oxygen sensor signals and
specifically “Delay 4” is the OSC related time which should correspond to the SID$06/OBDMID$21-$22 readings for OSC time.
Start
C atalyst diagnostics enable
conditions m et?
N
Y
S et front oxygen sensors to “rich”
Set rear oxygen sensors to “rich”
D elay 1
S et front oxygen sensors to “lean”
D elay 2
Set rear oxygen sensors to “lean”
D elay 3
S et front oxygen sensors to “rich”
D elay 4 (O S C related tim e)
Set rear oxygen sensors to “rich”
Figure 8: Scripting flowchart for catalyst diagnostics verifications
Figure 9 shows the catalyst diagnostic test data with 4 seconds of OSC time expected. From Figure 9(a), we can see that the oxygen
sensor signals (VO2B1S1, VO2B1S2) are synchronized with the ECU catalyst diagnostic enabling variable (ICENABLE). The time
for the rear oxygen sensor signal (VO2B1S2) staying “lean” (100 mV) before changing to “rich” (900 mV) is 4 seconds. Figure 9(b)
shows the SID$06/OBDMID$21-$22 readings, where TID$A1 and $A2 are OSC time readings for bank 1 and 2 respectively. TID$A1
has a value 0x 0F1A which converts to 3866 ms with USID$10 (1 ms/bit).

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Front O2 sensor changes to “lean” Rear O2 sensor changes to “lean”
which means a lot of oxygen storage in
catalyst
Set A/F ratio to “lean”
ICENABLE
Set A/F ratio to “rich”
It took about 4 seconds for rear O2
sensor change to “rich” which is an
indication of catalyst lifespan
(a) ECU internal variable data plot for catalyst diagnostics test

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Bank 1 catalyst OSC time = SID$06/OBDMID$21/ TID$A1 = 0x 0F1A
3866 ms (1 ms/bit for USID$10)
Bank 2 catalyst OSC time = SID$06/OBDMID$22/ TID$A2 = 0x 0F1A
3866 ms (1 ms/bit for USID$10)
(b) SID$06/OBDMID$21-$22 readings for catalyst OSC time
Figure 9
CONCLUSIONS AND FUTURE WORK
We have developed a mean value vehicle plant model in a HIL environment. The main purpose is to provide for the necessary
dynamic engine sensor signals so that the ECU will be able to run normal algorithms without being faulted out. With the HIL-based
testing system running with the vehicle plant model, we have explored and developed the capability to perform complex tests which
usually are done in vehicle conditions. Three different OBD II monitor tests, oxygen sensor monitor, VVT monitor, and catalyst
monitor, are developed in the HIL based testing system. The test results prove the developed mean value vehicle plant model is very
useful in ECU algorithm verifications.
For the future work, the model should be improved in at least the following areas:
• Improve oxygen sensor plant model accuracy so it can be used in more complicated tests such as fuel closed loop control;
• Improve transmission model to better estimate and model the vehicle torque dynamics.

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For the OBD II compliance, we will continue to develop the testing capability for other SAE J1979 listed sub-system monitor tests,
such as misfire and EVAP monitors.
ACKNOWLEDGMENTS
We would like to thank Charles Schulte from Delphi Powertrain for the helpful discussions on implementing catalyst monitor test
during this project.
REFERENCES
1. Elbert Hendricks and Spencer C. Sorenson, “Mean Value Modeling of Spark Ignition Engines”, SAE Technical Paper 900616,
1990.
2. Rajesh Rajamani, Vehicle Dynamics and Control, Springer, 2005.
3. http://en.wikipedia.org/wiki/Heat_of_combustion.
4. SI Engine Model on website: http://www.iau.dtu.dk/~eh/index.html.
5. http://en.wikipedia.org/wiki/Gas_constant.
6. John B. Heywood, Internal Combustion Engine Fundmentals, McGraw-Hill Book Company, 1988.
7. http://en.wikipedia.org/wiki/Heat_capacity.
8. Delphi Variable Cam Phasing Control Calibration Guideline.
9. SAE J1979 Document.
10. California Code of Regulations, Title 13, section 1968.2.
11. Yixin Chen, Rob Carpenter, Use of feedback control to improve HIL based ECU system function testing, SAE 2010 World
Congress, Detroit, USA, Apr. 13-15, 2010 (paper #: 2010-01-0663).
12. Delphi Idle Catalyst Monitor Diagnostic Calibration Guideline.
CONTACT INFORMATION
Yixin Chen, PhD
Senior Systems Engineer
yixin.chen@delphi.com
Jeffrey Pfeiffer
Senior Research Engineer
jeffrey.pfeiffer@delphi.com
Ken Simpson
On-Board Diagnostics Lead Engineer
ken.simpson@delphi.com