Xin Q. Diesel Engine System Design

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922 Diesel engine system design

parameters within an engine cycle by looking up the steady-state

turbocharger maps. Understanding the difference between the steady-

state turbine efciency and the transient efciency remains challenging.

There are large discrepancies between the conclusions from different

authors (Capobiano et al., 1989; Westin and Angstrom, 2002) who

obtained their data using different measurement methods (e.g., pulsating

gas stands or on engine in-situ) or CFD simulations. As to the turbine

ow rate, it was reported that the turbine under unsteady conditions has

a 3–6% higher swallowing ow capacity (i.e., the corrected mass ow

rate) than that measured at steady state (Capobiano et al., 1989). The

heat losses, volume, and thermal inertia of the exhaust manifold also

have signicant effects on the accuracy of turbocharger lag and engine

transient performance simulation.

3. Transient engine simulation and accurate prediction of driving cycle fuel

economy demand accurate engine control models that reect the engine

calibration set points in the speed–load domain, actual transient control

strategies, and the dynamic response of the sensors and the actuators.

4. Accurate simulations of cold start, warm-up and hot start transients are

important for analyzing transient emission cycles.

5. Transient simulation also requires accurate models of mechanical losses

from main friction components (i.e., piston–ring–liner assembly, bearings

and valvetrain) and parasitic losses from the accessories (Taraza et al.,

2007).

14.8.2 Real-time high-ﬁdelity versus mean-value transient

simulations

Real-time high-delity simulation with a detailed instantaneous in-cylinder

process and manifold gas wave dynamics is currently available. Mean-value

models have been used to conduct real-time simulations for engine control

design and vehicle driving cycle analysis prior to the real-time high-delity

modeling. The mean-value models use a simplied map-based approach without

calculating the in-cylinder cycle process at a crank-angle-level resolution.

Typically, volumetric efciency, indicated efciency, and exhaust energy

fraction are built in maps as functions of certain parameters such as engine

speed, fueling rate, intake manifold pressure, and air–fuel ratio. The mean-value

model has some disadvantages: (1) tremendous efforts need to be spent to run

the DoE data upfront to build the maps that are actually hardware-specic

and primitive; and (2) the model cannot predict the turbocharger lag transient

conveniently and accurately. Mean-value models may use a large time step

in the order of one engine cycle, while a detailed in-cylinder process model

requires 1–5∞ crank angle resolution. The theory on the mean-value model

is provided in Schulten and Stapersma (2003). There is a trend to develop

Diesel-Xin-14.indd 922 5/5/11 12:05:49 PM

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923Diesel engine transient performance and electronic controls

and use the real-time high-delity simulation to replace the mean-value

models for future transient analysis in order to increase the predictability

of the model. Moreover, in engine control simulations, software-in-the-loop

(SIL) and hardware-in-the-loop (HIL) are used to validate the algorithms

for both steady state and transient operations.

14.9 Sensor dynamics and model-based virtual

sensors

14.9.1 Classiﬁcation of engine sensors

The model-based controls of valves and turbochargers mentioned above

are essentially virtual actuators. In their equations, it is noted that the

engine temperature, pressure, and ow parameters can be from either actual

measurement or virtual sensor modeling. Model-based virtual sensors may

replace some actual physical sensors in the engine and aftertreatment systems

in order to reduce cost or to enable exible controls. For instance, switching

combustion mechanisms between different speed–load regions can be

achieved by predicting the in-cylinder parameters based on the combustion

process. The development of virtual sensors relies largely on thermodynamic

performance modeling.

Engine sensors can be classied into two categories, the physical sensors

(or real sensors) and the virtual sensors. The physical engine sensors usually

include: the crank position sensor and the cam position sensor for sensing

engine speed, TDC position, fuel injection timing and duration; the accelerator

pedal position sensor (SAE J1843, 2009); the pressure sensors for manifold

absolute air pressure (MAP), barometric ambient, DPF inlet pressure, oil, and

fuel; the temperature sensors for intake manifold air, exhaust manifold gas,

DPF inlet and outlet gases, oil, fuel, and coolant; the mass air ow (MAF)

sensor; the exhaust oxygen or lambda sensor; the NO

and NH

sensors used

in aftertreatment; and the oil and coolant level sensors.

The virtual sensors can be classied by algorithm into two types: physics

model based, and empirical data based (e.g., response surface models or

neural network models). Moreover, the virtual sensors can be divided into

steady-state virtual sensors and dynamic transient virtual sensors. They

require different computing algorithms. An example of the virtual sensors

is provided in Section 14.9.3.

14.9.2 Development needs for engine sensors

There are abundant needs for future virtual sensors in both steady-state and

transient for the next generation of ‘intelligent’ diesel engines. The needs

in future development include the following:

Diesel-Xin-14.indd 923 5/5/11 12:05:49 PM

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924 Diesel engine system design

∑ steady-state virtual MAF and MAP sensors

∑ other steady-state virtual sensors of gas ow, pressure, and temperature

which can be calculated using the 19 equations (4.39–4.57) governing

the engine air system described in Chapter 4

∑ a virtual engine torque sensor in torque-based controls of powertrain

central torque-demand coordination

∑ transient virtual sensors for in-cylinder real-time quantities, for example

air temperature, pressure and charge equivalence ratio during the

compression stroke in HCCI burning control

∑ engine-out NO

, PM/soot, and HC virtual sensors based on the in-cylinder

cycle process calculation with heuristic macro-parameter-dependent

emissions models

∑ virtual sensors for aftertreatment ow parameters and outlet emissions

built based on heuristic models

∑ combining all of the above to enable a whole engine model with affordable

computing time for integrated virtual sensing. A fully validated physical

model can then be used in real-time predictive algorithms of engine

controls.

Engine performance and emissions modeling is the foundation for building

these advanced virtual sensors. This area opens many challenges and

opportunities for system integration analysis and design.

Automotive sensors are introduced in Westbrook and Turner (1994). An

overview of diesel engine sensors (physical sensors) is provided by Challen

and Stobart (1998). Physics-based virtual sensors are discussed by Grimes et

al. (2005). Overviews on neural network based virtual sensing and sensors

are presented by Atkinson et al. (1998) and Nareid et al. (2005). EGR and

residue gas sensing has been researched by Müller et al. (2001) and Leroy

et al. (2009). Diesel engine air mass ow sensing has been investigated by

Höckerdal et al. (2008). Diesel exhaust temperature sensing is evaluated

by Culbertson et al. (2008) and Hori and Todo (2009). A virtual sensor of

barometric pressure for altitude detection is presented in Olin and Maloney

(1999). The diesel engine exhaust pressure sensor for DPF operation is

addressed by Ueno et al. (2008).

sensors have been investigated by Kato et al. (1999) and Orban

et al. (2005). NO

virtual sensors are presented by Re et al. (2005) and

Subramaniam et al. (2008). Particulate matter sensors are discussed in Allan

et al. (2003), Warey and Hall (2005), Hauser (2006), Diller et al. (2008,

2009), and Cai and Ma (2009).

Cylinder pressure sensors are discussed in Ulrich et al. (2001) and Hasegawa

et al. (2006). Virtual cylinder pressure sensors are explored in Palma et al.

(2004) and Wang et al. (2005).

Diesel-Xin-14.indd 924 5/5/11 12:05:49 PM

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925Diesel engine transient performance and electronic controls

14.9.3 Virtual sensor modeling of exhaust manifold gas

temperature

The virtual sensor for exhaust manifold gas temperature is important for engine

durability and EGR rate control. Its modeling is based on the thermodynamic

 rst law energy balance equation 12.2. Assuming



LHV

fuel

bas

e–

coolant

Wq =Wq

· (

Wq · (Wq

)

QB= QB

· (

HHVH

LHLHVLHL

fuel

miscell

aneous

miscellaneousmiscell

fuel

miscell

aneous

miscellaneousmiscell

LHV



)

QG= QG

· (

)

ÌÌÌ

14.3

where h

, B and G

are constants or known functions of engine speed, load,

air–fuel ratio, and fuel injection timing, the steady-state exhaust manifold

gas temperature can be calculated using equation 12.2 as:

turbin

e–

inle

fuel

nIMnI

LHV

pi,pi



(((

1 –

–

)

+ 1

fuel

,pe,

pexpe

– BG –



14.4

where



is the intake manifold mixture  ow rate of both fresh air and

EGR, and c

p,ex

and c

p,in

are the equivalent average speci c heats of the exhaust

 ow and the intake manifold  ow, respectively. It is observed that the exhaust

manifold gas temperature is a function of the ‘mass-to-fuel’ ratio

)



(/mm(/

fuel

In equation 14.4, one method to estimate the engine brake thermal ef ciency

is to calculate the engine brake power using the indicated torque and the

engine friction obtained from the torque-based controls. The prediction of the

engine indicated torque and the brake torque has been used increasingly as a

part of the coordinated shift control in automotive powertrains. In equation

14.4, an accurate estimation of B is a challenge. The value of B depends on

engine speed, load, charge mass-to-fuel ratio, fuel injection timing, intake

manifold gas temperature, coolant temperature, etc. One approach is to use

the equations of the in-cylinder cycle process along with the Woschni heat

transfer coef cient a

outlined in Chapter 4 to  rst calculate the transient

instantaneous exhaust manifold gas temperature, and then average it over an

engine cycle to obtain the steady-state exhaust manifold gas temperature. The

other approach is to develop heuristic models for the steady-state values of

B in order to build its sensitivity to other engine performance and operating

parameters.

Steady-state engine performance test data reveal that there is a correlation

between the temperature difference across the cylinder and the fuel-to-

charge mass ratio (Fig. 14.2), and the slope of the correlation line changes

Diesel-Xin-14.indd 925 5/5/11 12:05:50 PM

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926 Diesel engine system design

at different engine speeds. Such a correlation can be explained as follows. If

it is assumed that



is much greater than



fuel

and c

p,in

ª c

p,ex

, equation

14.4 is simpli ed to:

turbine–inle

tIMtI

LHV

TT – TT

(

qB(qB

1 –

qB1 – qB

qB – qB

–

)

fuel



14.5

where the coef cient C is a function of engine speed. A simpli ed empirical

model of the virtual sensor of exhaust manifold gas temperature can be

developed as follows:

turbin

e–

inle

tIMEtI

TT – TT

(

CC( CC

(

CC( CC

CC+ CC

)

NC) NC

NC+ NC

(

NC( NC

)

ME12ME

CC( CC

(

ME12ME

(

CC( CC

CC+ CC

(





fuel

14.6

where C

, C

and C

are the model tuning constants, and N

is the engine

speed. It should be noted that although the model equation 14.6 can predict

the steady-state exhaust temperatures reasonably well, it may not be able to

accurately predict the exhaust temperatures during fast transients due to the

lack of thermal-lag related factors in the model.

turbine–inlet

–T

(°F)

1100

1000

900

800

700

600

500

400

300

200

100

0 0.01 0.02 0.03 0.04 0.05 0.06

Fuel-to-charge mass ratio

700 rpm

800 rpm

1000 rpm

1200 rpm

1400 rpm

14.2 Illustration of virtual sensor – correlation between exhaust

manifold gas temperature and fuel-to-charge ratio.

Diesel-Xin-14.indd 926 5/5/11 12:05:51 PM

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927Diesel engine transient performance and electronic controls

14.10 On-board diagnostics (OBD) and fault

diagnostics

On-board diagnostics (SAE J1699-2, 1998; J1699-3, 2009; J1930, 2008) is

an engine control topic, and has been researched by Grimaldi and Mariani

(1999), Geraldo (2006), Vitale et al. (2007), Millo et al. (2009), and Fischer

et al. (2009).

Engine fault diagnostics is closely related to engine system design and

electronic controls because many parameters used in the diagnosis are

system-level performance parameters such as the instantaneous gas pressure,

temperature, ow, and the component rotational speeds. A system-level

dynamics model is usually required for model-based diagnostics. The area of

engine fault diagnostics is reviewed by Haddad (1984) and further investigated

by Schwarte and Isermann (2002), Macián et al. (2004, 2005, 2006a, and

2006b), and Yan et al. (2007). Diesel engine protection is reviewed by Fouch

and Gross (1991).

14.11 Engine controller design

The theoretical foundation of modern control engineering is summarized in

the classical textbooks authored by Ogata (2002, 2004), Palm III (2005),Dorf

and Bishop (2007), and DiStefano et al. (1990). Internal combustion engine

control and modeling is introduced in Guzzella and Onder (2004) and Guzzella

and Sciarretta (2005).

Engine controller design itself generally does not belong to the scope

of diesel engine system design. It falls within the job function of a control

engineer. However, dynamic engine system design cannot be implemented

without considering controller design. Diesel engine controls (e.g., air path

control, fuel system control, speed control, etc.) are nonlinear controls. Linear

design and reduced-order design are the common techniques used in controller

designs. PI, PD and PID controllers are still the most widely used controllers

in engines. Nonlinear numerical simulation of engine system dynamics may

provide a virtual conrmation of the controller behavior and the control

strategy, but the simulation itself is not a controller design tool. There is

a need to bridge the gap between engine system simulation and controller

design in order to promote the simulation-based controller design for nonlinear

controllers, especially for the air path and powertrain controls.

Engine controls and controller designs are presented in Chin and Coats

(1986), Weisman (1987), Tsai and Goyal (1986), Tuken et al. (1990), Scotson

and Heath (1996), Balfour et al. (2000), Christen et al. (2001), and Malkhede

et al. (2005). An adaptive torque controller design is reported by Fullmer

et al. (1992). PID controller design theories are presented by Strom and

Hagglund (1995, 2005). A PID controller for diesel engine speed control has

Diesel-Xin-14.indd 927 5/5/11 12:05:51 PM

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928 Diesel engine system design

been investigated by Mruthunjaya and Dhariwal (2000). Idle speed control

was studied by Memering and Meckl (1994, 2002). Diesel engine air system

(EGR–VGT) controller designs have been researched by Stefanopoulou

et al. (2000) and Utkin et al. (2000). A PID controller tuning for diesel

engine EGR–VGT control is investigated in Wahlström et al. (2008). A

PID controller design for a gasoline engine is introduced by Lauber et al.

(2002). The multi-input multi-output (MIMO) controller has been researched

by Stefanopoulou and Smith (2000) to reduce transient smoke for a marine

diesel engine. The comparison between a PID controller and a neural network

controller is provided by Tsuchiya et al. (2003). A fuzzy logic controller for

diesel engines is reported by Plianos et al. (2007). Robust control for marine

diesel engines is summarized by Xiros (2002).

14.12 Software-in-the-loop (SIL) and hardware-in-the-

loop (HIL)

Software-in-the-loop is introduced by Philipp et al. (2005) and Mitts et al.

(2009). Hardware-in-the-loop has been actively applied to engine/powertrain

control development and investigated in research (Isermann et al., 1998;

Rolfsmeier et al., 2003; Nabi et al., 2004; Shayler et al., 2005; Köhl and

Jegminat, 2005; Steiber et al., 2005; Wanpal et al., 2006; Schuette and

Ploeger, 2007; Wang et al. 2009; and Dhaliwal et al., 2009).

14.13 Cylinder-pressure-based controls

Good references on cylinder-pressure-based controls for diesel engines are

provided by Johnson et al. (1999), Nakayama et al. (2003, 2008), Klein et

al. (2007), and Husted et al. (2007). Due to limited space, the details are

not discussed here.

14.14 Homogeneous charge compression ignition

(HCCI) controls

HCCI control is an important area of combustion control. Detailed discussions

on HCCI are given in Section 9.8 in Chapter 9. For completeness, selected

references are provided here (Olsson et al., 2001; Zhao and Asmus, 2003;

Strandh et al., 2005; Bengtsson et al., 2006; Narayanaswamy and Rutland,

2006; Chauvin et al., 2006; Wang, 2007; Tunestål and Johansson, 2007;

Kumar et al., 2007).

14.15 References and bibliography

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Diesel-Xin-14.indd 928 5/5/11 12:05:51 PM

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929Diesel engine transient performance and electronic controls

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of a common rail diesel engine model into an industrial engine software development

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OBD-BR1 and OBD-BR2)’, SAE paper 2006-01-2671.

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measurements: state of the art, future prospects’, SAE paper 970532.

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board diagnostics using a free model technology’, SAE paper 1999-01-1224.

Grimes M R, Verdejo J R and Bogden D M (2005), ‘Development and usage of a virtual

mass air ow sensor’, SAE paper 2005-01-0074.

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engine for control: review and future trends’, SAE paper 2004-01-0423.

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inverse torque control of a diesel engine using adaptive mapping update’, SAE paper

2003-01-0397.

Guzzella L (2010), ‘Advanced control and engine management for future light-duty

diesel engines’, in Zhao H (ed.), Advanced Direct Injection Combustion Engine

Technologies and Development, Volume 2: Diesel Engines, Woodhead Publishing

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