Xin Q. Diesel Engine System Design

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378 Diesel engine system design

air ow occurring at high altitude. When the coolant temperature exceeds a

certain limit, the engine fueling rate is reduced in order to protect the engine

from being overheated. The derating obviously impairs the power capability

of the engine, and this may become a serious competitive issue when the

truck does not have enough power to maintain a high vehicle speed at high

altitude in mountain climbing. When the coolant becomes sufciently cool,

the derating is over and more fuel can be injected again to recover the

Engine brake torque (ft.lb)

700

600

500

400

300

200

100

–100

Engine lug curve

Engine speed (rpm)

Time history of engine operating points

500 1000 1500 2000 2500 3000 3500 4000

Time percentage (%)

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

650

1000

1600

2000

2400

2800

3100

3400

Engine speed (rpm)

250

450

651

Engine brake

torque (ft.lb)

US06

FTP75

HWY

60 mph SS

70 mph SS

(steady state)

5.18 Light-duty truck driving cycle simulation with partially loaded

weight (6500 lbs GVW).

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379Engine–vehicle matching in diesel powertrain system design

engine power. During high-altitude driving, such derating may occur several

times, as shown in Fig. 5.21. This vehicle driving simulation helps identify

the problems with heat rejection and derating control and can improve the

Vehicle acceleration

Vehicle speed

RAR 3

RAR 3.73

RAR 4.5

RAR 3

RAR 3.73

RAR 4.5

RAR 3

RAR 3.73

RAR 4.5

Lug Curve

Start of acceleration

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (sec)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (sec)

City-Suburban 0–60 mph

RAR cycle fuel acceleration

economy (mpg) time (s)

3 9.6 10.07

3.73 9.97 9.43

4.5 9.88 9.43

Intake manifold pressure

Vehicle speed

6500 lbs GVW

13000 lbs GVW

600 900 1200 1500 1800 2100 2400 2700 3000 3300 3600 3900

Engine speed (rpm)

0.00 3.72 7.43 11.15 14.87 18.58

Time (sec)

Acceleration (ft/s

)

Speed (mile/hr)

Pressure (bar, absolute)Speed (mile/hr)

4.2

3.6

3.0

2.4

1.8

1.2

0.6

62.5

50.0

37.5

25.0

12.5

0.0

5.19 Light-duty truck transient vehicle acceleration simulation on the

effect of rear axle ratio and vehicle weight.

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Engine lug

curve

4.4L

4.8L

4.4L

4.8L

4.4L

4.8L

4.4L

4.8L

Engine brake torque (ft.lb)

Engine speed (rpm)

600

500

400

300

200

100

4000

3500

3000

2500

2000

1500

1000

500

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Engine speed (rpm)

Vehicle speed (mph)

Engine brake torque (ft.lb)

600

500

400

300

200

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (second)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (second)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Time (second)

5.20 Light-duty truck transient acceleration simulation on the effect of engine displacement.

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Altitude (ft)

11,500

11,000

10,500

10,000

9,500

9,000

8,500

8,000

B_derate

A_derate (if derate to cool)

C_derate

B_derate_over

(when derating

is over)

0 1 2 3 4 5 6 7 8 9

Driving distance (mile)

A_derate

B_derate

C_derate

B_derate_over

B_derate

C_derate

B_derate_over

Vehicle speed

Engine-out coolant temperature

0 1 2 3 4 5 6 7 8 9

Driving distance (mile)

0 1 2 3 4 5 6 7 8 9

Driving distance (mile)

Coolant

temperature

threshold for

engine derating

Driving

station

index

Laskey

Gulch

Hamilton

Gulch

Entry of

Eisenhower

Tunnel

Driving

station

location

Highways 9

and 70

intersection

near

Silverthorn,

Driving

distance

(miles)

Altitude

(ft)

8600

9130

10400

11140

Road

grade

5.21 Vehicle driving simulation at high altitude and hot ambient on the effect of derating for coolant heat rejection

control.

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382 Diesel engine system design

system design accordingly to eliminate, minimize, or optimize these derating

effects based on all the trade-offs involved.

Vehicle powertrain dynamics performance simulation has also been

conducted by Moskwa et al. (1997, 1999), Assanis et al. (1999, 2000), Bi

et al. (2002), Vandenplas et al. (2003), Sahraeian et al. (2004), and Abel et

al. (2006).

5.4 Optimization of engine–vehicle powertrain

performance

In powertrain concept selection, with a given engine–vehicle–road

conguration, the above-mentioned steady-state and transient analyses can

be automated by computer simulation or programming, and the nal analysis

can be summarized as one high-level data point within a three-dimensional

domain of ‘fuel economy (e.g. mpg)’ vs. ‘vehicle acceleration capability (e.g.

the reciprocal of 0–60 mph acceleration time)’ and ‘engine emissions (NO

PM, HC or their weighted average)’, as illustrated in Fig. 5.22. Different

congurations can be repeated in a DoE analysis, including various drive

axle ratios, transmission gear ratios, gear numbers, torque converters, engine

rated power and speeds, engine displacements, emission control technologies,

or any other system-level parameters. Finally, all the summary data points

Fuel economy

(mpg)

Maximum

fuel economy

surface

Fuel

economy

(mpg)

Low emissions

envelope

This 3-D ‘box’ contains all possibilities

of DoE design factor combination

including non-optimized fuel economy.

These are maximum fuel economy

contours mapped from max surface by

optimization within design factor space

The envelope curves

reﬂect max capability.

New technologies also

move up the envelope

Design

target

Different

engine &

drivetrain

designs (‘C curve’)

Emissions

Acceleration capability (1/Dt

acc

)

High emissions envelope

Acceleration capability (1/Dt

acc

)

5.22 Method of powertrain performance optimization in engine

system design.

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383Engine–vehicle matching in diesel powertrain system design

(one from each conguration) are processed with DoE emulator optimization

techniques (i.e., building mathematical equations to link the input data; to the

output summary data, see Chapter 3 for details on DoE). Fuel economy can

be compared on a fair basis at xed acceleration capability and emissions.

The calculated trade-off envelope curves are identied as the maximum limit

of the system capability. The minimum fuel economy constant contours and

its parametric dependency can be computed. Such an optimization design

approach may greatly enhance the quality of powertrain concept selection

and help cascade the best concept to the lower level of design hierarchy with

a truly ‘top-down’ system approach.

5.5 Hybrid powertrain performance analysis

5.5.1 Importance of hybrid powertrain modeling in

engine system design

As an alternative to conventional powertrain technologies, hybrid electric

vehicles (HEV) and hybrid hydraulic vehicles (HHV) for both light-duty and

heavy-duty applications are being developed to meet the current stringent

regulations on fuel economy and emissions. The increasing interest in

hybrid commercial vehicles requires a detailed understanding of the hybrid

powertrain technologies and their system integration.

Diesel engine system design has been focusing largely on engine cycle

simulation analysis for the air system (valvetrain, turbocharger, and EGR

systems), heat rejection, and conventional vehicle performance for powertrain

integration. As the energy conversion technologies have become more

versatile, hybrid powertrains have become an integral part of diesel engine

system design. The main focus of the hybrid powertrain analysis in diesel

engine system design can be summarized as follows.

1. Use powertrain system simulation to evaluate the technical feasibility

of various hybrid architectures and quantify the performance benets

of hybridization in fuel economy, emissions reduction and vehicle

acceleration in different drive cycles and vehicle applications. Compare the

performance of hybrid powertrains with that of conventional vehicles.

2. Develop high-level system design specications for hybrid powertrains

in principal subsystem hardware sizing (i.e., engine, motor, battery).

3. Help evaluate supervisory control strategies by engine–vehicle

simulations.

4. Examine the sensitivity impact on performance due to the variations in

the design parameters of the battery and the electric machines caused

by design limitations, manufacturing tolerances, aging, etc.

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384 Diesel engine system design

5.5.2 Supervisory control strategies and hybrid-electric

conﬁgurations

The objectives of good supervisory control logic include the following: realizing

good driving performance, improving fuel economy, reducing emissions, and

maintaining good battery energy management. One of the critical issues in

the supervisory controller design is the battery life. It is imperative to keep

the state of charge within certain limits in order to avoid undercharge or

overcharge so that the battery life can be preserved. The operation of the

controller is determined by energy management rules that are a function of

the state of charge, the power demand and the braking power. Regenerative

braking provides the ability to recover the kinetic energy that otherwise

would be lost during the braking events by using the service brakes.

Typically, hybrid electric vehicles are classified into two basic

congurations, series and parallel hybrids, although other congurations

exist. In the series hybrid mode, there is no mechanical connection between

the internal combustion engine and the wheels. The engine is coupled with

an electric generator to produce electricity to charge the battery. The battery

is connected to an electric traction motor that drives the wheels. Due to

various operational possibilities of the traction motor (i.e., controlled as a

generator or motor), regenerative braking energy may be captured to charge

the battery.

In the parallel hybrid mode, both the engine and the electric motor are

coupled to the wheels to propel the vehicle. The engine supplies its power

mechanically to the wheels (like in a conventional vehicle), and the engine

is assisted by an electric motor if necessary. Some of the advantages of

the parallel mode operation include a reduced traction motor size, no need

for a generator, and the elimination of efciency losses in multiple power

conversion processes between the electric machines and the battery.

In the electric-vehicle mode, an external energy source (e.g., battery,

ultra-capacitor, fuel cell, etc.) and an electric motor are used for traction.

The electric vehicle mode does not produce emissions and can be operated

with high efciency and low noise and vibration. The deterioration in vehicle

acceleration and gradeability is probably the main disadvantage of the electric

vehicle mode, compared with a conventional vehicle.

5.5.3 Hybrid powertrain modeling

There are several commercial software packages available for hybrid powertrain

modeling (e.g., Gamma Technologies’ GT-DRIVE, LMS’s Imagine.Lab, and

Ricardo’s EASY-5 hybrid powertrain library). Vehicle models can be created

from various built-in library components in the object-oriented programming

environment in the software. The primary challenge in hybrid powertrain

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385Engine–vehicle matching in diesel powertrain system design

simulation is the modeling of supervisory control strategy to command the

engine and the hybrid components. For example, the key elements in a hybrid

electric powertrain performance model include the following (Fig. 5.23):

∑ a map-based engine model for BSFC, emissions, and gas temperatures

∑ driveline components (e.g., clutch, torque converter, transmission,

differential, driveshaft, axle, tire)

∑ electro-mechanical components (i.e., motor, generator)

∑ energy storage component (i.e., battery)

∑ electronic control and signal processing elements.

Once the vehicle architecture is created in the model, a controller for the

supervisory control strategy can be designed and implemented using detailed

electrical and control components. Typical scenarios integrated within the

supervisory control strategy include the following key factors:

∑ the transition process from electric launch to engine drive

∑ electric motor assist

∑ regenerative braking, braking load distribution (mechanical vs.

electrical)

∑ battery ‘state of charge’ (SOC) maintenance

∑ interactions, inter-dependence and interlocks between the above

factors.

5.5.4 Outlook of hybrid powertrain analysis in system

design

The simulation techniques in hybrid powertrain have reached a mature level

that can play a major role in hybrid technology development. The simulation

conducted in a ‘virtual world’ is critical for hybrid powertrain development

in order to ensure design optimization and cost reduction in testing and

development. The modeling of supervisory control strategy and optimizing the

complex subsystem interactions are two key challenges in hybrid powertrain

simulation. The optimization needs to occur at a powertrain and engine

system level in order to ensure the system specications of performance,

durability, packaging, and cost are considered simultaneously. Complete

references on hybrid powertrains (mostly for diesel engines) are organized

in Sections 5.6.2–5.6.4 below.

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5.23 Hybrid-electric powertrain modeling (GT-DRIVE model).

Controls

Battery and

e-machines

Engine

Vehicle

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387Engine–vehicle matching in diesel powertrain system design

5.6 References and bibliography

5.6.1 References and bibliography on conventional

vehicle powertrain

Abel A, Schreiber U and Schindler J (2006), ‘Engine and gearbox modeling and simulation

for improving the shifting behavior of powertrains with manual or automated

transmission’, SAE paper 2006-01-1641.

Assanis D, Bryzik W, Chalhoub N, Filipi Z, Henein N, Jung D, Liu X, Louca L, Moskwa

J, Munns S, Overholt J, Papalambros P, Riley S, Rubin Z, Sendur P, Stein J and Zhang

G (1999), ‘Integration and use of diesel engine, driveline and vehicle dynamics models

for heavy duty truck simulation’, SAE paper 1999-01-0970.

Assanis D, Filipi Z, Gravante S, Grohnke D, Gui X, Louca L, Rideout G, Stein J and

Wang Y (2000), ‘Validation and use of SIMULINK integrated, high delity, engine-

in-vehicle simulation of the International class VI truck’, SAE paper 2000-01-0288.

Balfour G, Dupraz P, Ramsbottom M and Scotson P (2000), ‘Diesel fuel injection control

for optimum driveability’, SAE paper 2000-01-0265.

Bata R, Yacoub Y, Wang W, Lyons D, Gambino M and Rideout G (1994), ‘Heavy duty

testing cycles: survey and comparison’, SAE paper 942263.

Bi X, Han S, Ma Z and Liu X (2002), ‘Transient modeling for heavy tracked vehicle

performance and fuel consumption under accelerating conditions’, SAE paper 2002-

01-0630.

Callahan B J, Kee R J, Mccartan C D, Fleck R, Kenny R G, Mackey D O (2003),

‘Drivetrain effects on small engine performance’, SAE paper 2003-32-0067.

Chana H E, Fedewa W L and Mahoney (1977), ‘An analytical study of transmission

modications as related to vehicle performance and economy’, SAE paper 770418.

Ciesla C R and Jennings M J (1995), ‘A modular approach to powertrain modeling and

shift quality analysis’, SAE paper 950419.

Ciesla C, Keribar R and Morel T (2000), ‘Engine/powertrain/vehicle modeling tool

applicable to all stages of the design process’, SAE paper 2000-01-0934.

Clark N N, Gautam M, Wayne W S, Nine R D, Thompson G J, Lyons D W, Maldonado

H, Carlock M and Agrawal A (2003), ‘Creation and evaluation of a medium heavy-

duty truck test cycle’, SAE paper 2003-01-3284.

D’Anna T, Govindswamy K, Wolter F and Janssen P (2005), ‘Aspects of shift quality

with emphasis on powertrain integration and vehicle sensitivity’, SAE paper 2005-

01-2303.

Dopson A, Taitt D and Sandford M H (1995), ‘Powertrain systems denition process’,

SAE paper 950811.

Dorey R E and Holmes C B (1999), ‘Vehicle driveability – its characterisation and

measurement’, SAE paper 1999-01-0949.

Dorey R E, McLaggan J D, Harris J M S, Clarke D P and Gondre B A C (2001),

‘Transient calibration on the testbed for emissions and driveability’, SAE paper

2001-01-0215.

Ehlbeck J M and Mayenburg M V (1991), ‘Increasing heavy-duty truck fuel economy’,

SAE paper 912662.

Everett R L (1971), ‘Measuring vehicle driveability’, SAE paper 710137.

Ferraz L A S, Coelho E P D, Araujo N S S, Sarno I C and Silva A C (2001), ‘Powertrain

process to translate customer perceptions into technically driven database’, SAE paper

2001-01-3838.

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