Investigation of the Impact of Fuel Properties on Particulate Number Emission of a Modern Gasoline Direct Injection Engine

2018-01-0358 Published 03 Apr 2018

Investigation of the Impact of Fuel Properties

onParticulate Number Emission of a Modern

Gasoline Direct Injection Engine

Mohammad Fatouraie, Mario Frommherz, and Michael Mosburger Robert Bosch LLC

Elana Chapman and Sharon Li General Motors LLC

Robert McCormick and Gina Fioroni National Renewable Energy Laboratory

Citation: Fatouraie, M., Frommherz, M., Mosburger, M., Chapman, E. et al., “Investigation of the Impact of Fuel Properties on Particulate

Number Emission of a Modern Gasoline Direct Injection Engine,” SAE Technical Paper 2018-01-0358, 2018, doi:10.4271/2018-01-0358.

Abstract

asoline Direct Injection (GDI) has become the

preferred technology for spark-ignition engines

resulting in greater specic power output and lower

fuel consumption, and consequently reduction in CO

emission. However, GDI engines face a substantial challenge

in meeting new and future emission limits, especially the

stringent particle number (PN) emissions recently introduced

in Europe and China. Studies have shown that the fuel used

by a vehicle has a signicant impact on engine out emissions.

In this study, nine fuels with varying chemical composition

and physical properties were tested on a modern turbo-

charged side-mounted GDI engine with design changes to

reduce particulate emissions. e fuels tested included four

fuels meeting US certication requirements; two fuels meeting

European certication requirements; and one fuel meeting

China 6 certication requirements being proposed at the time

of this work. Two risk safeguard fuels (RSG), representing the

properties of worst case market fuels in Europe and China,

were also included. e particle number concentration of the

solid particulates was measured in the engine-out exhaust

ow at steady state engine operations with load and speed

sweeps, and semi-transient load steps. e test results showed

a factor of 6 PN emission dierence among all certication

fuels tested. Combined with detailed fuel analyses, this study

evaluated important factors (such as oxygenates, carbon chain

length and thermo-physical properties) that cause PN emis-

sions which were not included in PMI index. A linear regres-

sion was performed to develop a PN predictive model which

showed improved tting quality than using PMI.

Introduction

ntroduction of the GDI engines into the light-duty gasoline

eet has been an enabler to achieve the reduction targets

of CO

emissions. One of the signicant challenges for the

new internal combustion engines has been the particulate

emissions. Locally rich combustion leads to the formation of

soot, resulting in high particulate emissions, especially at the

most efficient engine operation points in terms of fuel

consumption, i.e. low engine speeds and medium to high

loads. Improved engine component designs, updated controls

and optimized calibration have resulted in significant

improvements in particle emissions reduction; nevertheless,

the impact of the fuel and its chemical composition and

physical properties must also be considered. Many researchers

have been assessing the impact of the fuel properties on engine

out emissions, in addition to trying to determine an appro-

priate correlation that would provide some predictive method

of the fuels physical and chemical properties to the engine out

emissions [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11].

Aikawa et al. introduced Particulate Matter Index (PMI)

as a model to predict particle emissions from fuel composition

(measured by detailed hydrocarbon analysis), including all

individual components of the fuel [1]. ere are many other

researchers who have developed methods to determine a

numerical relationship for fuel properties and the propensity

of the fuel to produce PN or PM [5, 7, 8]. As Chapman and

coworkers have shown, all provide a correlation to PMI

number with some having a more direct relationship than

others, and some having a stronger relationship to vehicle out

emissions [5]. Recent work by Leach and co-workers, and

Wittmann and Menger show stronger correlations of the PMI

to engine out PM and PN, as well as demonstrating that a

model tool can be developed to help translate fuel properties

to predicted engine out emissions [7]. Wittmann and Menger

have compared their approach to many others in the industry

and shown a stronger correlation, even to those correlations

that try to use simple and single fuel properties [7]. ey

showed that correlation would be based on 14 properties, and

developed a model around a single engine. Leach showed that

a detailed analysis could be avoided by using composition

classes and the bulk fuel dry vapor pressure equivalent (DVPE)

in a new particle number index (PNI), essentially improving

on the work of Aikawa and co-workers [8]. ese papers seem

to indicate that in addition to the fuel properties, some

NREL/CP-5400-71483. Posted with permission. Presented at WCX 18: SAE World Congress Experience, 10-12 April 2018, Detroit, Michigan.

2 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

combination of engine hardware, fuel preparation, engine

calibration settings and their eect on the engine out emis-

sions explain why the PMI number does not always show a

strong correlation to the PN or particulate mass (PM) emissions.

Additionally, researchers have been looking at the impact

of oxygenates on the PM/PN emissions. Leach et al. tested three

dierent alcohols, and found dierent eects on PN emissions,

with methanol being the highest [11]. N-butanol performed

similar to the model base fuel gasoline. Overall, PN increased

for each of the oxygenated fuels. Conversely, work by Sakai and

Rothamer showed that the addition of ethanol caused a decrease

in the engine out particulate in proportion to the ethanol

content [9]. Yinhui et al. showed that a fuel with 10% ethanol

produced limited improvement on particulate emission

compared to reducing the aromatic and olen content in the

gasoline [6]. ey also showed that the 10% ethanol increased

the PN emissions at low load conditions. Mohd Murad and

coworkers studied the impact of methanol and showed that the

methanol does not lead to low levels of particulate number

emissions, specically across a range of fuel injection timing

[10]. ey also showed that the addition of heavier components

to a fuel does not alter the spray structure under ash boiling

conditions. Also, they demonstrated that a higher initial boiling

point does not necessarily lead to higher particulate emissions,

and that the light end of the gasoline range did not inuence

a rich mixture or a liquid lm that promoted an increase in the

particulates [10]. Ratcli and coworkers examined a broad

range of oxygenates and found that in certain cases oxygenates

could produce higher PM than hydrocarbons with equivalent

double bond equivalent and vapor pressure at 443K - the fuel

parameters used in calculating PMI [12].

Various regions around the world have specic local

emission regulations that require the use of specic certica-

tion fuels which must be considered in the engine design. is

presents a very challenging task for Automotive OEMs, and

so continued eort is devoted to determine an appropriate

correlation or predictive model relating the fuels physical and

chemical properties to engine out emissions. is work focuses

on the investigation of the inuence of chemical and physical

fuel properties relating to particle number emissions on an

GDI engine with design improvements to signicantly reduce

engine-out PN emissions to levels approaching what is

required by Euro6 PN emission legislation [13]. It is necessary

to reassess published particulates vs. fuel properties correla-

tions which were generated on older engine designs.

Furthermore, understanding the PN performance dierences

among certication and worse case fuels in dierent markets

will allow Automotive OEMs to avoid engine hardware prolif-

eration and reduce calibration eort.

ExperimentalSetup

Test Engine and Dyno Setup

e engine used in this study was a 2.0 L turbocharged four

cylinder direct injection spark ignition engine. The side

mounted fuel injector and the centrally located spark plug

were in a longitudinal arrangement. The Bosch 6-hole

HDEV5.2 injectors were mounted between the two intake

valves in each of the four cylinders, with a spray targeting that

was spreading diagonally downwards into the combustion

chamber. e engine specications are detailed in Table 1.

e engine design improvements to reduce PN emissions

included updated combustion chamber and piston designs

that enhanced air/fuel mixing and reduced fuel rich pockets.

e engine piston cooling was optimized to avoid unnecessary

cold piston surfaces that contributed to increased fuel lm on

the piston top. e injector spray target and injector seat

design went through multiple iterations, and achieved low PN

emissions and reduced soot deposits both inside and outside

the spray holes on the injector tip.

e test engine was setup on an engine dynamometer with

well controlled coolant, oil, fuel, ambient air and intercooler

outlet temperatures. In the majority of the engine testing, coolant

and oil temperatures were set at 90 (±3) °C, simulating hot engine

operations. e ambient air temperature was set at 23(±1) °C, the

intercooler outlet temperature at 31(±2) °C, and the fuel tempera-

ture at 21(±1) °C, all values simulating the typical engine opera-

tion in vehicles. e engine oil used for this test was a 5W-30

available in the market. e in-cylinder pressure was measured

using piezoelectric pressure transducers from AVL, through a

series of machined ports in the cylinder head. e automated

test procedure for the engine dynamometer was controlled with

iTest and INCA was used for the engine control unit command,

both communicating with IndiCom data acquisition and evalu-

ation soware. Engine parameters were resolved to crank angle

and the emission data were acquired at 1Hz frequency.

Fuels and Fuel Analysis

A total of nine fuels were tested in this study. e data for the

fuels is found in the Appendix. e fuel test matrix includes

the certication fuels for Europe, China, and the United States

and additionally two Risk Safeguard fuels (RSG) which are

representing the worst-case fuels for Europe and China, namely

RSG E10 and RSG M15. e RSG fuel blends were dened as

the most aggressive fuels to simulate the worst-case situations

that engines could face. Certication fuels are used to check

the compliance of the region-specic emission standards by the

respective emission regulation authorities. e North American

fuels in this test are represented by CARB LEV II, CARB LEV

III, EPA Tier 2, and the EPA Tier 3 premium blends. A special

China 6 Premium fuel blend, based on the dra of the China

6 Premium fuel specications, was also formulated by GM for

this study. e European certication fuels, Euro 5 and Euro

TABLE 1 Test engine speciﬁcation

Engine type 4-stroke, 4-valve

Displacement [cm

] 1998

Bore x stroke [mm] 86 x 86

Compression ratio [-] 9.5:1

Aspiration Turbo-charged

Fuel delivery DI, side mounted

Injector Bosch 6-hole

Maximum rail

pressure

[bar] 350

INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION 3

6, complete the test fuel matrix. ese fuels covered a wide range

of fuel properties that are important to PN emissions.

All the certication fuels and the RSG E10 blend were

sourced from Gage Products Company. RSG M15 fuel was

blended by Haltermann Carless. All test fuels have been

subjected to Detailed Hydrocarbon Analysis (DHA), by a

modified ASTM D6730 method, measuring individual

components in the fuels. e oxygenated hydrocarbon content

was analyzed using the procedure described in reference [14]

and ASTM D4815, and the distillation curves for the fuels

were measured with the ASTM D86 method at the GM Fuels

laboratory. e PMI number was calculated using the standard

calculator as shared from Honda, Aikawa and co-workers. In

this study, the oxygenates for the DHA were corrected using

the data produced by the ASTM D4815 method. is provides

a more representative PMI number, by adjusting all the

components in the DHA and thus the PMI nal number.

Additionally, the heat of vaporization (∆H

vap

) of the fuels

were measured at the National Renewable Energy Laboratory

(NREL), using the methods described by Chupka et al. [15].

Briey, ∆H

vap

was measured using a TA Instruments (Newark,

DE) simultaneous DSC/TGA (SDT) Q600 instrument. e

instrument was calibrated per manufacturer’s specications.

e cell constant was further calibrated using deionized water.

ree replicate analyses of water were run and a ratio of the

measured value to that reported in the literature was calcu-

lated to generate a calibration factor. All ∆H

vap

measurements

were then multiplied by this factor. Samples were introduced

via a gas tight syringe into a 90 uL platinum pan which was

equipped with an aluminum lid with a laser drilled pinhole,

75 um in diameter. Samples were held at ambient lab tempera-

ture for the duration of the test and a nitrogen purge ow of

50 mL/min was used to aid in sample evaporation. In some

cases where the test run time was over 3 hours, the pinhole

lid was not used to avoid baseline dri (these samples are

noted). To calculate the ∆H

vap

, the heat flow signal was

corrected for the heat ow associated with the empty pans by

subtracting this value from the total measured heat ow. e

area under the curve was calculated and divided by the sample

mass to obtain the ∆H

vap

of the sample. e samples were run

in duplicate in a randomized order for DSC/TGA analysis.

Particle Counter

Particulate number emissions were measured in the engine-

out exhaust flow using a Horiba MEXA-2100SPCS. The

measurement method is based on the requirements described

in Revision 4 of the UN/ECE Regulation No.83, which is a

uniform provision concerning the approval of new vehicles

regarding the emission of pollutants. To achieve sucient

repeatability of the measurement, this regulation enforces the

count of only solid particles in the engine exhaust which are

larger than 23 nm. e exclusion of volatile particles stems

from the fact that the dilution conditions signicantly aect

the formation of Nucleation Mode particles due to condensa-

tion of volatile substances such as sulfates and soluble organic

fractions. is is being achieved by having heated sample lines

(to 150°C) and Volatile Particle Remover (VPR) at 300-400°C

aer the rst dilution stage. e total dilution ratio of 2500

was set during the tests in two stages.

Test Procedure and PN

Measurement

All measurements are performed at the engine test laboratory

at the Robert Bosch LLC facility in Farmington Hills,

Michigan. An automated test procedure, shown in Figure 1,

was implemented to assess the PN emission of the fuels. Tier

3 premium fuel was chosen as the baseline fuel for this study

and the engine calibration was performed to optimize the fuel

injection timing, targeting to minimize the PN emission and

specific fuel consumption. Single homogenous injection

strategy was used for the fuel injection timing calibration to

reduce the complexity of the test matrix. e same engine

calibration settings were used for all test fuels.

To address the concern whether the PN emission perfor-

mance at a single steady state point of 2000rpm/10bar BMEP

would suciently represent real world engine operations, the

visitation frequency of the engine speed and load during a

typical drive cycle was calculated and shown in Figure 2. e

distribution of the engine load at 2000 RPM is shown in the

right panel. It is clear that the engine spent a substantial

amount of time at 2000rpm and 8-10 bar BMEP.

FIGURE 1 Automated Test Procedure.

FIGURE 2 Engine load and speed visitation points during a

drive cycle.

4 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

It is well known that PN measurements have a high uncer-

tainty and it is not easy to ensure adequate PN measurement

repeatability and accuracy so that the impact of fuel properties

can be determined with sucient condence. A few measures

were taken in this study to reduce the impact of engine

hardware performance shi and improve PN measurement

repeatability and accuracy:

1. Before testing each fuel, the injectors were cleaned in

an ultrasonic bath for an hour to remove all the soot

deposits from the injectors and reset the engine PN

performance to that of clean injectors.

2. A 20 hour endurance test was performed for each fuel

to ensure that the level of soot deposit on the injectors

and other parts of the engine and measurement

equipment has achieved equilibrium. e PN

emissions were measured every 5 minutes during

this test.

3. e average of the last 4 hours of the endurance test

data at 2000rpm and 10bar BMEP were used to assess

the impact of the fuels. is not only ensured that the

engine had sucient time to reach desired operating

conditions and stable PN performance, but also

allowed a large data ensemble of PN measurements.

e average values and the standard deviations are

shown in Figure 3.

4. e test order for dierent fuels were randomized and

each fuel was tested more than once non-

consecutively. e Tier-3 premium were tested

periodically throughout the entire study and

repeatable results were observed.

For the dierent test fuels, the same valve timings and

injection strategies were used that were optimized for Tier-3

premium fuel. e engine was operated at the stoichiometric

condition by injecting the same energy content into the

combustion chamber. e closed loop lambda control of the

ECU controlled the stoichiometry during the test based on

the oxygen concentration in the exhaust. Due to dierences

in the carbon, hydrogen and oxygen composition of the fuels,

the lower heating values are dierent among the studied fuels.

e ignition timing was adjusted to achieve the maximum

brake torque (MBT), such that the CA50, where 50% of the

cumulative heat release has been converted, is approximately

8 °CA aTDC with the baseline fuel, Tier-3 premium and kept

constant for the other tested fuels. A summary of the engine

parameters for the three types of testing is shown in Table 2.

ResultsandDiscussions

Although the main focus of this study was to assess the impact

of certication fuels and worst case fuels in dierent markets

on PN emissions, it is desirable to establish a correlation

between fuel properties and PN emissions that provides a tool

to project the PN performance of a certain hardware for

dierent markets.

As shown in Figure 3, among the certication fuels, LEV II

and LEV III produced the lowest PN results and were not signi-

cantly dierent from one another. As expected, the highest PN

emissions were from the RSG fuels with RSG M15 a factor of

roughly 2.5 higher compared to China 6 Premium. e dierence

in the heat of combustion between the fuels, based on various

levels of oxygenates, as well as the fuel densities led to dierent

injection durations. This difference resulted in 10% longer

duration of injection for RSG M15 compared to Tier 2 premium

fuel. From injector tip inspection aer the test and measured

injector energizing time during the 20hr test, none of the fuels

had produced major soot deposit inside the spray holes that

caused fuel ow shis. e maximum injector energizing time

shi of the worst PN performing fuel, the RSG M15, is around 1%.

Comparison of the PM Index

e PM Index, developed by Aikawa et al.[1], is the most estab-

lished particulate matter and number predictor for gasoline

fuels. Regardless of engine type or test cycle, the PMI predicts

the relative tendency of a specic gasoline blend to produce

PM. Detailed compositional information about the fuel with

the volatility and structural characteristics of its constituents

FIGURE 3 PN emission average values, normalized to

emissions of Tier 3 certiﬁcation fuel, during the last 4 hours of

endurance tests

TABLE 2 Test parameters

Load sweep

20 hour

test

Transient

load step

Lambda [-] 1 1 1

RPM rpm 2000, 3000 2000 1500

BMEP [bar] 1-20 10 1 and 8

SOI [°CA

bTDC]

290

1 bar: 300

290 260-300

CA50 [°CA

aTDC]

8 8 8

Coolant

temperature

[°C] 90 90 90

INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION 5

are combined in this method. As seen in the formula below,

PMI is calculated based on the double bond equivalent (DBE),

the vapor pressure at 443 Kelvin (V.P. (443K)) and the weight

fraction (W

) of every single hydrocarbon and oxygenate

component of the fuel. In addition, Aikawa et al. also

concluded that the PMI can be correlated to either mass or

number based particle emission.

Index

DBE

VP K

()

443.

(1)

Detailed hydrocarbon analysis is required to get the

necessary data for the PMI calculation. e fuel analysis and

PMI calculation was performed by the GM Fuel Lab and at

NREL and the results are shown in Table 3. e calculated

PMI for the LEV certication fuels are the lowest in this

comparison. e calculated PMI for RSG E10 is signicantly

higher than the rest of the tested fuels, which is approximately

twice as high as the calculated index for Euro 6 fuel. e PMI

for the worst-case scenario fuel for China, RSG M15, is 13 to

16% higher than that of China 6 Premium certication fuel.

Because of dierences in the implementation of the DHA

analysis, including dierences in the approach to calculation

of the vapor pressure at 443K, the NREL PMI values are

slightly dierent, but exhibit identical trends.

Figure 4 shows the PN emissions as a function of PM

index. e coecient of determination (R

) for the correlation

is low, indicating that PMI is not adequate to predict the varia-

tion in PN emissions for these fuels. is may not be surprising

in that all but one of the fuels contain oxygenates, which are

not explicitly accounted for in the calculation of PMI. ere

may also be other factors that are not captured by PMI such as

density, viscosity, and surface tension and their eect on fuels

spray penetration, spray angle and breakup, as well as ∆H

vap

Removal of the two highest PM emitting fuels (the RSG fuels)

improves the R

to 0.74 supporting the idea that PMI is not

capturing all the important fuel related factors for this group

of fuels. In particular, the high PN emissions for RSG M15

relative to other fuels with similar PMI values (such as Tier 2

and Tier 3) is of interest. To aid in subsequent discussion of

these results, additional fuel properties are presented in Table 4.

Tier 2 vs Euro 6

In order to dissect the discrepancy in the PN prediction of the

PMI, the parameters aecting the PN levels for Tier 2 and

Euro 6 fuels are investigated in more details by examining the

factors that make up PMI, as well as those that are not directly

included in the PMI calculation. e Tier 2 and Euro 6 fuels

had fairly similar PMI values (1.66 and 1.28), but PN emissions

are almost twice as high for Euro 6. e chemical group

composition of the two fuels with respect to the carbon

number are shown in Figure 5. e peak of the overall hydro-

carbon composition of Tier 2 is at the carbon number of 8,

whereas Euro 6 exhibit a at composition with the maximum

at C

. e overall aromatic content of Euro 6 is less than Tier

2, contributing to the higher PMI of the later fuel. However,

the level of aromatic hydrocarbons with more than 8 carbon

atoms (C

aromatics) is higher in Euro 6 which contains

11.7 % C

aromatics compared to only 8.51 % in Tier 2.

TABLE 3 PM Index of the tested fuels

Fuel type

PM Index

GM Fuel

Lab

PM Index

NREL

Oxygenate

Type

Oxygenate

% D4815

LEV II 1.12 1.24 MTBE 10.88

LEV III

Premium

1.17 1.30 Ethanol 10

Tier 2 1.66 1.66 None 0

Tier 3

Premium

1.68 1.76 Ethanol 9.71

Euro 5 1.89 1.75 Ethanol 5.07

Euro 6 1.28 1.64 Ethanol 9.87

China 6

Premium

1.33 1.49 MTBE 8.17

RSG E10 2.77 3.07 Ethanol 9.87

RSG M15 1.5 1.73 Methanol

MTBE

15.3

7.1

FIGURE 4 Normalized PN emissions at 2000rpm and 10bar

BMEP as a function of average PMI

TABLE 4 Heat of vaporization and lower heating value

measurement results

Fuel type

Lower Heating

Value

[MJ/kg]

HOV DSC/TGA

[kJ/kg]

Density at

15°C

LEV II 42.54 381 0.743

LEV III

Premium

41.78 425 0.749

Tier 2 43.18 372 0.740

Tier 3

Premium

41.69 423 0.749

Euro 5 41.91 421 0.753

Euro 6 41.73 461 0.752

China 6

Premium

42.51 385 0.759

RSG E10 - 426 0.740

RSG M15 - 513 0.739

ASTM D240 from Supplier Certicate of Analysis

6 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

Another reason for the lower PMI of Euro 6 is due to the

ethanol content. In PMI calculation, each component is

linearly weighted in the blend, which neglects the synergist

or antagonistic behavior of individual components in the fuel

blend. Low boiling and thus high vapor pressure components

in the fuel are under predicted for near-azeotropic ethanol-

gasoline blend. Although several studies have conrmed a

reduction of PM emissions using ethanol instead of gasoline,

there are investigations showing dierent results [16, 17, 18,

19]; Vuk et al. presented a considerable reduction in PM emis-

sions with E10 (10% ethanol in gasoline) compared with the

E0 fuel but increasing with E30 and E50 [20].

Considering the physical eects, ethanol has a higher

density and viscosity. is results in poor spray atomization

and deeper spray penetration [18]. e lower heat of combus-

tion of ethanol further inuences the spray penetration of Euro

6, due to a higher amount of fuel that is required at equal

engine operation points. e injector energizing times (as a

proxy for injection duration) of Euro 6 was around 2 % longer

than Tier 2 which could result in deeper spray penetration and

thereby more piston wetting at the same SOI. is change of

the spray is not included in the PMI although it can signi-

cantly aect PN emissions. Another reason for the opposite

trend in the PMI and the PN can be the ∆H

vap

of the fuels,

seen in Table 4 Heat of Vaporization measurement results. e

∆H

vap

of Euro 6 is around 24 % higher than that of Tier 2. In

order to vaporize, the fuel needs a certain amount of thermal

energy to transform from the liquid into the gaseous phase.

is means that Euro 6 needs more thermal energy to fully

vaporize and thereby causes colder conditions in the combus-

tion chamber. Due to these colder conditions, the evaporation

of the heavy end of the fuel would be impeded. [4, 21].

Due to the correlation with the vapor pressure and linear

weighting, PMI doesn’t capture the entire evaporation

behavior - especially the low distillate end. e distillation

curves of Tier 2 and Euro 6 fuels are presented in Figure 6.

e gure is intentionally plotted as a function of temperature,

since during the engine operation the fuel temperature would

be directly aected by the coolant temperature. e evapora-

tion of the last 30 % of the fuels is similar but the nal boiling

point of Tier 2 is higher. e higher the boiling point and the

larger the percentage of fuel concentrated in its heavy end,

the higher is the probability for the presence of liquid fuel due

to non-timely evaporation. Due to the large proportion of

ethanol in Euro 6, the initial vaporization vary signicantly.

At 90 °C, equivalent to the engine operation temperature,

almost 60 % of Euro 6 is evaporated. Flash boiling might occur

under these conditions. Heat transfer in the cylinder head and

pressure increase in high-pressure pumps increase the temper-

ature of the liquid fuel before the injection. In homogeneous

injection mode, the injection takes place during the intake

stroke and therefore the fuel is injected into approximately

the manifold pressure which can be lower than the saturation

pressure, especially under throttled conditions. High volatility

components of the fuel become superheated under these

conditions and ash vaporization would occur. is eect is

known to greatly inuence the atomization and vaporization

process [22, 23]. However, ash boiling can also adversely

aect the fuel vaporization. During the injection process, the

temperature of the fuel assimilate to the intake air temperature

and the saturation pressure rises above the ambient pressure.

Flash boiling components cause spray plume collapse and

therefore lead to a deeper spray penetration and thereby to

wetting of the piston and the cylinder walls [24, 25].

Based on the discussion above, the higher PN emissions

observed for Euro 6 relative to Tier 2, in spite of their similar

PMI values, are caused by several factors that are not included

in the PMI model. ese likely include the combined eects

of reduced lower heating value in the Euro 6 requiring longer

injection duration, increased ∆H

vap

causing reduced tempera-

ture during evaporation and thus hindering evaporation of

heavy aromatics, and the potential for ash boiling to occur

for the ethanol blend.

Tier 2 vs RSG M15

Tier 2 and RSG M15 are predicted to emit similar PN emissions.

However, based on the PN results in Figure 4, there is a big dier-

ence between these. Many of the same considerations discussed

for the Tier 2 - Euro 6 comparison apply here as well. While

energy density of RSG M15 was not measured, given this fuels

high oxygenate content its lower heating value will be signi-

cantly lower than that of Tier 2, requiring longer injection

FIGURE 6 The eect of ethanol on the distillation curve for

Euro 6 and Tier 2 fuels.

FIGURE 5 Volume fraction distribution of chemical

structures as a function of carbon number for of Euro 6 and

Tier 2 fuels.

INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION 7

duration at the same BMEP. RSG M15 also has the highest ∆H

vap

of the fuels studied. Figure 7 shows the carbon histogram of

Tier 2 and RSG M15. M15 fuel blend contains higher content of

larger aromatics but also 15 % methanol and 7% MTBE,

mirroring the higher heavy aromatics content and high alcohol

content of Euro 6. e high heavy aromatics combined with the

high ∆H

vap

may be a cause of the much higher PN of RSG M15.

Regarding the distillation behavior, similar evaporation

behavior can be found between RSG M15 and Euro 6. e

distillation curves of Tier 2 and RSG M15 are shown in

Figure 8. e study by Qin et al. showed a signicant reduc-

tion of PN emissions with the addition of methanol [13]. e

low boiling oxygenate components of the fuels cause a high

percentage of evaporated fuel at low temperatures. Due to

the lower boiling point of methanol versus ethanol, approxi-

mately 65 % of the RSG M15 is evaporated at 90 °C.

RSG M15 vs RSG E10

e two risk safeguard fuels RSG M15 and E10 show the

largest discrepancies in the PMI and PN measurement results.

While PM emissions are similar, the E10 has a PMI that is

more than 1.2 PMI units higher. Figure 9 exhibits the results

of the hydrocarbon analysis of RSG M15 and E10. e overall

hydrocarbon composition seems to be identical at rst glance,

but particularly the distribution of the higher molecular

weight hydrocarbons diverges. RSG E10 includes approxi-

mately 23% C

aromatics whereas RSG M15 only consist

of 14%. Within the tested fuels, the amount of C

aromatics is highest with E10. When comparing the C

11+

aromatics, an even bigger dierence is shown. e percentage

of C

11+

aromatics of the E10 fuel is approximately 6 %

whereas the percentage of M15 is only 0.5 %. Particularly the

high amount of heavy aromatic hydrocarbons leads to the

highest PMI for RSG E10. RSG M15 has the highest ∆H

vap

which is signicantly higher than the value for RSG E10. is

may contribute to the similar level of PN observed for these

fuels, in spite of the higher heavy aromatic content of RSG E10.

e distillation curves of RSG M15 and RSG E10 are

shown in Figure 10. Except the bigger dierence between 50

and 70 % distillation, which can be attributed to the higher

percentage of low boiling oxygenate components of M15, the

two fuels similarly evaporate with a small oset. Due to the

bigger proportion of high boiling point components of E10,

the respective percentages vaporize at higher temperatures

over the whole range. Based on the distillation curves, a clear

statement on whether the higher nal boiling point of E10 or

FIGURE 7 Volume fraction distribution of chemical

structures as a function of carbon number for of RGS M15 and

Tier 2 fuels.

FIGURE 8 Distillation curves of Tier 2 and RSG M15.

FIGURE 9 Volume fraction distribution of chemical

structures as a function of carbon number for RSG E10 and

RSG M15 fuels.

FIGURE 10 Distillation curves for RSG M15 and RSG E10

8 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

the higher probability of ash boiling of M15 has a stronger

impact on PN emissions cannot be given.

e dierences in the fuels in terms of vaporization and

ash boiling would reect signicantly in terms of sensitivity

to the injection timing. More advanced injection timing leads

to higher chances of spray impingement with the piston and

consecutively diffusion flames with high PN emission.

Asweep of injection timing was performed as the part of the

semi-transient portion of the automated test at 1500 RPM and

8 bar BMEP. Normalized PN emissions for each fuel as a

function of injection timing is plotted in Figure 11. e values

for all fuels are normalized to the highest emission of the RSG

E10 fuel at the most advanced injection timing and shown in

the top panel (a). e bottom panel (b) is scaled to highlight

the certication fuels more clearly. Most of the certication

fuels did not show signicant sensitivity to the start of injec-

tion for the studied range (from 300 °CA bTDC to 260 °CA

bTDC), with Euro 5 fuel exhibiting the largest reduction which

was in the order of 2.

RSG E10 exhibits the highest sensitivity to the start of

injection, which can be attributed to the highest nal boiling

point, high heavy aromatics content, and high heat of vapor-

ization. e results indicate that with a retarded start of injec-

tion and avoiding the piston impingement, the PN emission

could be reduced by an order of magnitude. is reduction

comes at the cost of charge inhomogeneity and increases the

specic fuel consumption by 1 %.

As highlighted in Figure 2, the engine resides at mid-load

points for a signicant time at 2000 RPM; therefore engine

load sweep was performed to investigate the sensitivity of the

fuels to dierent engine load operations. e result of the PN

emissions for the load sweep between 4-10 bar are shown in

Figure 12. e results are normalized to the PN emission of

the Tier-3 premium fuel as the baseline. All of the certication

fuels exhibit similar sensitivity to the engine load at this speed

and the PN emission ranking of them doesn’t change. e

injection timing during these loads were kept constant at 290

°bTDC and as shown in Figure 11, the certication fuels didn’t

exhibit a signicant sensitivity to the start of injection timing.

e RSG-M15 and RSG-E10 fuels don’t follow the same trend.

is range of load sweep represents the transition between

the throttled operations to the boosted conditions which

would aect the ash boiling characteristic of the fuels.

Signicant calibration eort would be required to develop a

robust SOI for these fuels at dierent loads.

Regressions Analysis

Besides the investigation of the impact of chemical and physical

fuel properties on particulate number emissions in gasoline

direct injection engines, the development of a PN predictive

model was the main goal of this work. Aikawa et al. introduced

the PMI and it became the established PN and PM predictive

model [1]. However, complex calculation and insucient PN

and PM prediction for oxygenated fuels, which have been

conrmed in this study, leave potential for improvement.

e measured PN was set as the dependent variable and

the chemical components and the physical properties as the

independent ones. Due to the small number of dependent

variables and a huge selection of the independent ones, it was

impossible to calculate a general model to predict the PN

emissions. e regression analysis provided a wide variety of

possible models which perfectly correlate to the PN. In order

to counteract, the selection of the independent variables was

based on previous research and results of this study and

thereby reduced to a small number, which resulted in the

coecient of determination of 0.92. e values of the param-

eters of the nal model are rounded for clarity. e loss in the

coecient of determination due to rounding of the parameters

is minor and does not signicantly aect the nal result. e

nal model and the selected variables are shown below and

FIGURE 11 Sensitivity of PN emission to the start of

injection timing. The PN values are normalized to RSG E10 at

300 °CA bTDC. Panel b is scaled for clarity.

FIGURE 12 Normalized PN emission at mid-load conditions

at 2000 RPM. The data is normalized to the PN emission of

Tier-3 fuel.

INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION 9

explained subsequently. (PNR is referring to the PN

Regression Equation)

PNRA OV

=*-*+*+*+*

+*-*

0030055 0 001 0 004

0010001 0 00005

.. ..

.. .BB

[]

(2)

A = Aromatics C9/C9+ [Vol. %]

O = Total percentage of oxygenates [Vol. %]

V = Heat of vaporization [kJ/kg]

B = Final boiling point [°C]

E = Vol. % of fuel evaporated @ 90 °C [%]

C9/C9+ Aromatic Hydrocarbons e most signicant

correlation to the PN was found with the C

and C

aromatic

hydrocarbons. In the fundamental research by Kobayashi et

al., PM from gasoline and hydrocarbons of various chemical

structures were measured and it was found that the aromatic

ame showed the highest concentration [4]. e reason for this

is that the aromatic compounds are harder to evaporate and

relatively slower to decompose than other hydrocarbons.

Furthermore, in the case of decomposition, aromatics may

disintegrate into unsaturated alkyl compounds such as acety-

lenes which serve again as precursors for the formation of a

benzene ring. ese results were also shown in several studies

with GDI engines [20, 26]. Detailed investigations of the eects

of the aromatic contribution on PM resulted in a higher propen-

sity to produce PM with higher aromatic species, although the

tested fuels consist of a wide range between 0 and 20 % of C

aromatics, the low molecular weight aromatics showed no eect

on PN, which could indicate that the heavy aromatics do not

disintegrate into smaller ones but directly take part in the PAH

and thereby the soot formation mechanisms. Furthermore, it

was found that only the percentage of the high molecular weight

aromatics markedly aect the PN emissions.

Total Percentage of Oxygenated Hydrocarbons A

majority of studies have supported the signicant eects of

oxygenate hydrocarbons on PN emissions. Gasoline fuel with

oxygenated compounds tend to produce less PN emissions,

but negative effects on PN were also shown with higher

percentage of oxygenates [20]. e highest percentage of

oxygenates of the tested fuel is RSG M15 with 19 Vol. %. is

high percentage could be an explanation for the worse PN

emissions of RSG M15. Although oxygenates are included in

the PMI, they merely play a minor role in the nal value. Due

to the lack of double bonds and the high vapor pressure

compared to other components, the oxygenates are roughly

counted as parans and the potential of positive eects on

PN is not included. at is why the oxygen containing hydro-

carbons are selected as a variable for the regression analysis.

Final Boiling Point e physical properties with the signif-

icant eects on the PN emissions are gured out to be the nal

boiling point, the percentage of fuel evaporated at 90 °C, and

the heat of vaporization. In the PMI calculation, the boiling

point is indirectly accounted for each component of the fuel

through a logarithmic correlation with the vapor pressure.

However, the evaporation behavior of the fuel blend is dierent

than the evaporation of each single component added. As can

be seen with Tier 2 compared to Euro 6 and also RSG M15, the

distillation of the fuel blend behaves in a dierent way. Due to

the respective higher long-chain aromatic content of Euro 6

and RSG M15, an assumption could be made that this leads to

a higher boiling point. However, in both cases the nal boiling

point of Tier 2 is higher and the eects can be explained due

to the near-azeotropic behavior of the fuel mixture.

Vol. % of Fuel Evaporated @ 90 °C e value for the

percentage of fuel evaporated at 90 °C is a characteristic value,

which can be found from the distillation curve of the fuel.

is value is suitable to quantify the eects of ash boiling

and therefore was set as an independent variable. e disparity

between the C9/C9+ aromatics and the PN of RSG M15 and

E10 can be explained due to ash boiling.

Heat of Vaporization A certain amount of thermal

energy is required to vaporize the fuel, which is dened as the

heat of vaporization or enthalpy of vaporization. In a direct

injection engine, the required thermal energy is extracted from

the charge air. e eect of charge cooling enables achieving

higher eciency through increased compression ratio by miti-

gating knock. ereby, the CA50 be adjusted closer to the

optimum of 8 °CA aTDC. However, the cooling eect would

cause undesirable conditions regarding the formation of soot

in the cases of spray impingement with the piston surface or

the cylinder liner. e higher the heat of vaporization, the

worse the evaporation of the fuel which leads to a higher prob-

ability of diusion ames on the engine surfaces.

Conclusions

e eects of chemical and physical properties of gasoline fuel

on particulate number emissions have been studied in a

modern GDI engine. e certication fuels of Europe, China,

the US, and additionally two Risk Safe Guard fuels were used

for this investigation. ere are a few conclusions that can

drawn from this study:

• A factor of 6 in PN dierence was observed during the

steady state operation point at 2000 RPM and 10 Bar

BMEP among all certication fuels tested. e same

trend was observed at dierent engine operating points.

e risk safeguard fuels exhibited the highest PN

emission and highest sensitivity to the injection

timing calibration.

• e long-chain aromatic hydrocarbons, more precisely the

aromatics, showed the biggest impact on particulate

number emissions. is nding is consistent with

previously published investigations and the C

aromatics. Despite the emphasis in the PM Index

calculation, the contribution of the smaller-chain aromatics

on PN emission were insignicant at the tested condition.

• e oxygenated compounds blended in the fuels have

non-linear eects. e fuel bound oxygen lowers the

sooting tendency of the fuel but the eect of ethanol

addition in terms of aecting the spray development and

vaporization of the fuel can result in higher PN emission.

• Based on the regression analysis performed on the

various physical parameters of the fuel blends, nal

10 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

boiling point, percentage of fuel evaporated at 90 °C, and

heat of vaporization indicated to be the best suitable

variables to characterize the evaporation behavior.

Higher nal boiling point and higher heat of

vaporization values result in worse evaporation and

therefore lead to higher particulate emissions due to

diusion ames of liquid lm on the engine surfaces.

Based on the ndings of this study, a PN predictive model

was successfully developed. Including the aromatic and

oxygenated hydrocarbons as two fuel components and the

three physical properties, nal boiling point, the percentage

of fuel evaporated at 90 °C, and the heat of vaporization. It is

nevertheless necessary to validate the PNR with dierent

engines and fuels to further improve this model.

In summary, it is clear that the gasoline fuel chemical and

physical properties impact the PN emissions from an engine.

e variation in certication fuels and market fuels (which

the certication fuels in some cases represent) present a signif-

icant challenge for OEMs to meet future emissions regula-

tions. e wide range of PMI number with those fuels and the

impacts of the oxygenates present challenges not understood

before. Since the certication fuels are representations of

market fuels, an improvement in market fuel quality variation

can substantially reduce the risk of vehicle in-use PN

emission compliance.

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Contact Information

Dr. Elana Chapman

General Motors LLC

Elana.Chapman@gm.com

Dr. Sharon X. Li

General Motors LLC

Sharon.Li@gm.com

Dr. Mohammad Fatouraie

Robert Bosch LLC

Mohammad.Fatouraie@us.bosch.com

Acknowledgements

Research at the National Renewable Energy Laboratory was

conducted as part of the Co-Optimization of Fuels & Engines

(Co-Optima) project sponsored by the U.S. Department of

Energy - Oce of Energy Eciency and Renewable Energy,

Bioenergy Technologies and Vehicle Technologies Oces.

Co-Optima is a collaborative project of several national labo-

ratories initiated to simultaneously accelerate the introduction

of aordable, scalable, and sustainable biofuels and high-

eciency, low-emission vehicle engines. Work at the National

Renewable Energy Laboratory was performed under Contract

No. DE347AC36-99GO10337.

anks also goes to the support from the Analytical

Chemists at General Motors for their assistance in the testing

and compiling of the data: Doug Conran, Andrew Zabik,

Jessica McGahan, and Justin Pletzke; additionally the support

of the Analytical Chemist at NREL, Earl Christensen for his

eorts on the DHA and PMI calculations. We want to espe-

cially thank the help and support of Je Jetter from Honda

for the collaboration with the use of the PMI method and tools.

Deﬁnitions/Abbreviations

DVPE - Dry Vapor Pressure Equivalent

ECU - Engine Control Unit

GDI - Gasoline Direct Injection

HDEV - Name of a Bosch type of Fuel Injector

HOV - Heat of Vaporization

PM - Particulate Matter

PMI - Particulate Matter Index

PN - Particulate Number

PNR - PN Regression Equation

RSG - Risk Safeguard Fuel

RSG E10 - Risk Safeguard Fuel 10% Ethanol

RSG E15 - Risk Safeguard Fuel 15% Methanol

VP - Vapor Pressure

12 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

AppendixFuelProperties

TABLE A1 Fuel Properties: Heat of Vaporization and Detailed Hydrocarbon Analysis with Carbon Number by Aromatics shown

with PMI number

Euro 5

LEV III

Prem

Tier 3

Prem LEV II Euro 6 Tier 2

China 6

Premium RSG E10 RSG M15

Oxygenate Type Ethanol Ethanol Ethanol MTBE Ethanol None MTBE Ethanol Methanol

MTBE

Heat of

Vaporization DSC/

TGA (KJ/kg)

421 425 423 381 461 372 385 426 513

CARBON# ( Vol %) Aromatics Aromatics Aromatics Aromatics Aromatics Aromatics Aromatics Aromatics Aromatics

1 -- -- -- -- -- -- -- -- --

2 -- -- -- -- -- -- -- -- --

3 -- -- -- -- -- -- -- -- --

4 -- -- -- -- -- -- -- -- --

5 -- -- -- -- -- -- -- -- --

6 0.01 0.70 0.58 0.83 0.01 0.58 -- -- 0.27

7 19.00 5.74 6.42 6.42 14.06 18.95 17.05 -- 4.85

8 0.06 6.71 6.15 9.30 0.11 4.31 6.48 1.28 1.74

9 12.36 6.09 5.68 6.04 6.39 3.94 9.48 8.59 7.82

10 2.36 2.85 4.52 2.05 3.79 2.46 0.93 8.26 6.12

11 0.85 0.67 1.35 0.47 1.00 1.38 0.79 4.36 0.27

12 0.41 0.33 0.66 0.26 0.52 0.73 0.04 1.50 0.16

13 -- 0.00 0.00 -- 0.00 0.00 0.01 -- 0.03

TOTAL 35.04 23.10 25.36 25.36 25.88 32.34 34.79 23.99 21.27

C9 + Aromatics 15.98 9.95 12.21 8.81 11.70 8.51 11.24 22.71 14.38

C10 + Aromatics 3.62 3.85 6.53 2.77 5.31 4.57 1.76 14.12 6.56

PMI Number 1.89 1.17 1.68 1.12 1.28 1.66 1.33 2.77 1.5

INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION 13

TABLE A2 Fuel Properties as listed on the Certiﬁcate of Analysis from the supplier

Fuel

Property Euro 5

LEV III

Prem Tier 3 Prem LEV II Euro 6 Tier 2

China 6

Premium RSG E10 RSG M15

RON D2699 96.9 99.8 99.5 100.5 96.3 96.5 96.6 >101 104

MON

D2700

87.1 88.8 88 88.5 85.6 86.8 85.9 88.6 87.7

RVP @100F

D5191 (psi)

8.52 7.2 9.2 6.71 8.27 8.83 8.5 10.6 13.9

Aromatic %

D1319/5769

32.2 23.4 23.1 25.1 26 31.3 24.83 22.1

Oleﬁn %

D1319/6550

5.1 6.5 4.8 10.1 8.4 12.5 18.2 22.8

Oxygenate

% D4815

5.07 10 9.71 10.88 9.87 0 8.17 9.87 15.3

7.1

Oxygenate

Type

Ethanol Ethanol Ethanol MTBE Ethanol None MTBE Ethanol Methanol

MTBE

Speciﬁc

Gravity

@15.56

D4052

0.7532 0.7494 0.7433 0.7401 0.7588

Density @15

C (g/cc)

0.7529 0.7491 0.752 0.74 0.7393

T10 ( C )

D86

58.3 52.4 59.7 53.3 57.2 51.9 48.7 39.4

T90 ( C )

D86

155.9 160.7 144.3 160.7 149.4 160.8 185.9 166.6

T95 ( C )

D86

168.9 177.5 177.6 186.1 171.6 196.1

FBP ( C )

D86

185.2 201.9 182.8 190 212.8 195.5 221.7 197.7

Evaporated

at 70C

34.1 44.9 45.6 63.2

Evaporated

at 100C

52 57.4 55.2 67.3

Evaporated

at 150C

84 87.5 79.2 87

14 INVESTIGATION OF THE IMPACT OF FUEL PROPERTIES ON PARTICULATE NUMBER EMISSION

FIGURE A1 Chart of the test fuel densities (ASTM 4052).

FIGURE A2 Distillation Curve (ASTM D86) Chart for all fuels.

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ISSN 0148-7191