Composition of gasoline

In this version of the model, reformulated gasoline can be characterized in more detail, and more accurately, than in the original version. The user can specify any volumetric mixture of alkanes, aromatics, olefins, ETBE, MTBE, methanol, and ethanol (Table C.2). The model calculates the carbon content, density, and heating value of the specified gasoline. On the basis of new data from the Auto/Oil Air Quality Improvement Research Program (1995), I have changed the specification of conventional and reformulated gasoline in the model (Table II).

Oxygenates in reformulated gasoline

The model now calculates in complete detail the greenhouse-gas impact of oxygenates added to gasoline. The model considers three different kinds of oxygenates: 1) methanol or ethanol added directly to gasoline; 2) methanol or ethanol plus isobutylene made from field butanes in NGL plants, made into MTBE or ETBE additive; and 3) methanol or ethanol plus isobutylene made from crude oil in refineries, made into MTBE or ETBE additive.

There are three main parts to the calculation of GHG impact of oxygenates. First, the model calculates the mass (not volume) of crude oil displaced by methanol or ethanol added directly or embedded in MTBE or ETBE, and by isobutylene derived from butanes from NGL plants. The model then reduces the mass of crude oil that must be recovered, transported and refined to make a unit of gasoline. (Chemical properties for alcohols and ethers are from the CRC Handbook of Chemistry and Physics [1975].) This reduction in the amount of crude oil that must be recovered, transported, and refined, per unit of gasoline produced, reduces fuelcycle CO2-equivalent emissions attributable to reformulated gasoline.

Stork and Singh (1995) model how much of the isobutylene in ETBE and MTBE will be derived from NGLs, and how much will be derived from crude oil, for several scenarios regarding the composition of reformulated gasoline. (They make separate estimates for summer gasoline and winter gasoline.) On the basis of their estimates, I assume that 7.5% of the isobutylene used to make ETBE, and 5% of the isobutylene used to make MTBE, comes from crude oil.

Second, the model estimates complete fuelcycle CO2-equivalent emissions from the production and transport of butanes (used to make isobutylene) from NGL plants, and from the production and transport of methanol or ethanol added directly or made into MTBE or ETBE. I have assumed that the methanol or ethanol come from the same fuel production-and-delivery cycle as does the methanol or ethanol that is used directly by alcohol-fueled vehicles. Also, I assume that any crude-oil derived butane (used to make isobutylene in the third kind of oxygenate above) would have been produced anyway and used in conventional gasoline, and hence would not change refinery use of energy or crude oil compared to the conventional gasoline baseline.

The model is based on the information in Singh and McNutt (1993) and Stork and Singh (1995), with three refinements: First, as mentioned above, the model estimates complete fuelcycle emissions from the production of oxygenates and oxygenate components. For example, it includes emissions from the use of electricity to produce the natural gas from which the butane used to make isobutylene is derived. Second, the model calculates chemical properties of mixtures and components from primary data on characteristics of organic compounds. Third, the model gives an emissions credit for the use of the fuel gas that is a byproduct of MTBE and ETBE production. I assume that this fuel-gas is composed partly of the hydrogen that must be removed from the butane to make isobutylene, and partly of the leftover butane or butane derivatives (more butane is consumed than is needed for the reaction stoichiometry). This suggests to me that the fuel-gas byproduct, which is considerable, is rather like refinery gas. Therefore, I assume that the fuel-gas byproduct displaces refinery gas.

Recently, Hesse et al. (1993) have described an integrated plant which produces ethanol, methanol, ETBE, and MTBE from input corn and butane. The butane is made into isobutane and then into isobutylene, and the corn is made into ethanol. The CO2 off-gas from ethanol production and the hydrogen off-gas from isobutylene production are combined to make methanol. The methanol and ethanol can be combined with the isobutylene to make MTBE or ETBE. This process probably results in lower CO2 emissions than the conventional process assumed above because the methanol is made from CO2 from the corn section that otherwise would be vented.

CO2 from biomass-derived ETBE

A previous version of the model did not deduct from total CO2 emissions any CO2 emitted from the biomass-derived ethanol portion of ETBE additive. This has been corrected.

Mixtures of reformulated gasoline and conventional gasoline

I have expanded the ability of the model to calculate complete CO2-equivalent fuelcycle emissions from mixtures of: 1) conventional gasoline and reformulated gasoline; 2) gasoline and methanol (from coal, natural gas, or wood); and 3) gasoline and ethanol (from corn or wood). In the original version of the model, one could specify either all reformulated gasoline or all conventional gasoline, but nothing in between. Now, the user can specify any volumetric mixture of reformulated and conventional gasoline. You input the characteristics of conventional gasoline, the characteristics of reformulated gasoline, vehicular g/mi emission factors for conventional gasoline, and g/mi emissions factors for reformulated gasoline. The model calculates the characteristic of the specified fuel mixture, the fuel economy of a vehicle using the mixture, and average g/mi emissions from a vehicle using the mixture. The average g/mi emissions are calculates simply as: input g/mi emissions for conventional gasoline multiplied by the fraction of miles driven on conventional gasoline, plus input g/mi emissions for reformulated gasoline multiplied by the fraction of miles driven on reformulated gasoline. The mileage fractions are calculated on the basis of the specified fuel mix and the thermal efficiency of each fuel.

Mixtures of alcohols and gasoline

The original version of the model could not estimate emissions from a mixture of biomass-derived methanol and gasoline (from crude oil). Now the model can estimate emissions from any mixture of biomass-derived alcohol (methanol or ethanol) and gasoline. The model calculates g/mi emissions from vehicles using these mixtures in the same way that it calculates g/mi emissions from vehicles using mixtures of conventional gasoline and reformulated gasoline (explained above).

Mixtures of soy diesel and petroleum diesel

The model estimates complete fuelcycle emissions from any mixture of soydiesel and petroleum diesel. The user specifies the volume percentage of soy diesel in the fuel, and the model calculates the energy characteristics of the fuel mix, the fuel consumption of the vehicle, the emissions of the vehicle, and the upstream emissions associated with fuel production.

LPG intermediate results

In the intermediate calculation of grams-CO2 equivalent fuelcycle emissions per million-BTU (Table 7), the LPG column has been separated into LPG from natural gas, and LPG from oil.

Source of LPG

In recent years the fraction of propane and butane being supplied from refineries rather than natural-gas-liquids plants has been increasing. For 1995, I estimate that 43% of the LPG supplied to the market came from refineries (EIA, Petroleum Supply Annual 1995, 1996) (compare with assumptions in Appendix G of Volume 2). This change in the source of LPG causes an increase in fuelcycle GHG emissions of less than 1%.

Heating value, carbon content, sulfur content, and ash content of coal

In the original report I assumed 57.17 lbs-C/106-BTU for generic coal and coal for methanol plants, and 57.35 for coal for power plants (Table C.1), on the basis of data presented in Table C.6 of Volume 2 (DeLuchi, 1993). I also assumed that the C/BTU value is independent of the rank of the coal. Recently, the EIA (Hong and Slatick, 1994) analyzed 5,426 coal samples, and concluded that in 1992 all U.S. coal averaged 56.65 lbs-C/106-BTU, and coal for power, 56.68 lbs-C/106-BTU. They also demonstrated that C/BTU content in fact varies slightly with the rank of the coal: generally, as the rank decreases, from bituminous to sub-bituminous to lignite, the C/BTU content increases slightly. This, coupled with the shift in consumption from high-sulfur Eastern bituminous coal to low-sulfur Western sub-bituminous coal, has a resulted in a steady increase in the average C/BTU content of coal consumed in the U. S. (Hong and Slatick, 1994; EIA, Annual Energy Review 1995, 1996). The limitations on sulfur emissions specified by the Clean Air Act Amendments of 1990 will continue the shift from high-sulfur Eastern bituminous coal to low-sulfur Western sub-bituminous coal (Hong and Slatick, 1994; EIA, Annual Energy Outlook 1996, 1996).

In light of these and other new data, I have projected that the heating value, carbon content, sulfur content, and ash content of coal will decrease steadily through the year 2015. The new base-year values, and the projected rates of change, are shown and documented in Table III. These changes in the specifications of coal increase coal-cycle emissions by about 0.5%.

Carbon content, specific gravity, and sulfur content of crude oil

On the basis of ultimate analyses of 1982 crude oil samples, the EIA (Emissions of Greenhouse Gases in the United States 1987-1994, 1995) has estimated the carbon content of crude oil as a function of the sulfur content and API specific gravity:

density in g/gal = SG.1000.3.7854,

where:

SG = the specific gravity calculated from the input API parameter value, as above

The resulting g/gal densities generally are higher than I had assumed originally: 3250, versus 3191 (Table C.1).

Composition of refinery gas

The EIA (Emissions of Greenhouse Gases in the United States 1987-1994, 1995) reported and discussed four estimates of the composition of refinery gas. Three of their four estimates are from source "E" of Table C.5 of Volume 2 (DeLuchi, 1993). The fourth estimates a composition of 12.7% H2, 28.1% CH4, 17.1% C2H6, and 11.9% C3H8. The EIA (1995) concludes that refinery gas generally must comprise mainly "less valuable" feedstocks, such as CH4 and CO. This conclusion, and the new (fourth) estimate cited above, are consistent with the original assumptions of Table C.3 of Volume 2 (DeLuchi, 1993). Consequently, I have not changed the composition factors for refinery gas.

Composition of natural gas

I have changed the volumetric composition of natural gas slightly, to the "typical" composition reported by the EIA (Alternatives to Traditional Transportation Fuels, 1994).

Characteristics of soy diesel

Soy diesel has been added to the model. The characteristics of 100% soy diesel fuel are taken from Table A-1 of the Appendix to this report.

Fuel economy, drive cycle, and vehicle weight

In the original version of the model, one entered the following:

• the fuel economy of baseline gasoline vehicle

• the fuel economy of the baseline diesel vehicle

• the thermal efficiency of the AF ICEVs relative to that of the baseline gasoline ICEV

• the thermal efficiency of the AF ICEVs relative to that of the baseline diesel ICEV

• the efficiency of the EV powertrain relative to the efficiency of the ICEV powertrain

• weight parameters

• the effect of weight on fuel economy

Given these input data (Table 2), and an equation that calculated the weight of the baseline vehicle on the basis of a statistical relationship between weight and EPA city/highway mpg, the model calculated the weight and energy use of the all of the vehicles. Note that in order for the weight/fuel-economy equation to have given the correct result, the input mpg had to have been the combined city/highway mpg. To compare the vehicles over any other drive cycle -- say, to compare EVs with ICEVs in city driving -- one had to enter the city mpg of the baseline gasoline vehicle but then overwrite the weight-calculation equation with the weight calculated from the city/highway mpg. This work-around was cumbersome.

I have rewritten the model to correctly calculate weight and fuel-use for all vehicles for any user-specified mix of city and highway driving. The user now supplies the following input data for the baseline gasoline and diesel vehicles:

• the fuel economy of baseline vehicle using conventional gasoline, in city driving

• the fuel economy of baseline vehicle using conventional gasoline, in highway driving

• the fuel economy of the baseline diesel vehicle, in city driving

• the fuel economy of baseline diesel vehicle, in highway driving

• the city fraction of total miles driven by light-duty ICEVs

• the city fraction of total miles driven by heavy-duty ICEVs

• the weight of the baseline diesel vehicle

With these inputs, the model calculates the fuel economy of the baseline gasoline and diesel vehicles over the specified driving cycles. The weight of the baseline gasoline vehicle is calculated on the basis of the original statistical relationship between weight and the 45/55 fuel economy. (The 45/55 fuel economy is calculated from the input data above.)

To calculate the energy use of the alternative-fuel vehicles (AFVs) , the model first calculates the drivetrain efficiency and the weight the AFVs relative to the drivetrain efficiency and weight of the baseline gasoline or diesel vehicle. The relative drivetrain efficiency, expressed as the mi/BTU efficiency of the alternative-fuel engine or EV drivetrain divided by the mi/BTU efficiency of the baseline gasoline or diesel engine, is projected according to the following equation:

Eq. B-1

Table IV shows the input values of MV, BV, and k, and the calculated value of PV for the year 2015.

The relative weight, expressed as the difference between the weight of the AFV and the weight of the baseline gasoline or diesel vehicle, is calculated as the change in the weight of the powertrain and body plus the change in the weight of the fuel storage system. The change in the weight of the fuel storage system, in turn, is calculated from input data on the range of the vehicle and the characteristics of the fuel storage system (discussed more below). The total change in weight is multiplied by a weight compounding factor (0.065 lbs-structure/lb-extra-weight) to account for the extra structure associated with any extra weight.

Finally, given the calculated or input fuel economy and weight of the baseline gasoline and diesel vehicles, the calculated relative drivetrain efficiency and vehicle weight, and the user-specified relationship between changes in weight and changes in energy use, the model calculates the actual energy use of the AFVs over the specified drive cycle (see below). The model assumes that alternative-fuel ICEVs follow the same drive cycle as baseline gasoline or diesel vehicles. However, the user now can specify a separate drive-cycle for EVs.

Formula to calculate energy efficiency of AFVs

In Appendix A and Appendix B of Volume 2 (DeLuchi, 1993) , the following equation was used to calculate energy efficiency:

where:

Mi = 106-BTU/mi efficiency of AFV i

1+Ti = the powertrain efficiency of AFV i relative to that of baseline petroleum vehicle p

Wf = % decrease in fuel economy (in mi/BTU) per 1% increase in vehicle weight (0.55)

Wi = the extra weight of AFV i compared to petroleum-fuel vehicle p

Wp = the total driving weight of petroleum-fuel vehicle

MPGp = the miles-per-gallon fuel economy of petroleum-fuel vehicle p

Dp = the 106-BTU/gallon heating value of petroleum fuel p

This equation is wrong. The correct equation is:

This has been corrected in the model.

Electric vehicles

The ratio of the EV drivetrain efficiency to the ICEV drivetrain efficiency (Table 2) has been changed, on the basis of a detailed second-by-second drive-cycle energy-use analysis of EVs and ICEVs. The drivecycle energy-use model is documented in detail in a separate report, Motor-Vehicle Energy-Use Model (Delucchi, 1995). I developed this model in part because in the original report I found that the relative drivetrain efficiency was the most important and uncertain variable in the EV analysis, and needed to be characterized much better. Table IV shows the relative drivetrain efficiency estimated by the second-by-second energy-use model. On the basis of the results of Table IV, I make the assumptions shown in Table IV.

Several other assumptions and calculation methods regarding EVs have been changed:

i) the lifetime of the vehicle has been reduced from 153,000 miles to 118,800 miles (1.1 times longer than the ICEV, rather than 1.42 times) (Table P.2), on the basis of a reconsideration of the likely longevity of EVs);

ii) the lifetime of the battery now is calculated as:

where:

L = the battery life in miles

CL = the cycle life (see below)

MU = the urban driving range (see below)

DoD = the average depth of discharge per cycle (I assume 75%)

iii) the specific energy of the battery, a key determinant of the weight and hence efficiency of the EV, has been added as an input variable. With this, the weight of the battery now is calculated as:

where

WB = the weight of the battery (lbs)

EC = the energy consumption of the EV, from the battery terminals (kWh/mi; calculated as a function of the drivetrain efficiency and the weight of the vehicle)

R = the driving range of the vehicle (projected to increase as the specific energy and performance of the EV improve [Table VI])

1000 = Wh/kWh

2.205 = lbs/kg

DoD = the depth of discharge at the desired driving range (1.00)

SE = the specific energy of the battery (Wh/kg; projected for every year from 1994 to 2015, with eq. B-1 above [Table VI])

iv) the cycle life of the battery (Table P.2), the efficiency of the battery, the efficiency of recharging, the relative weight of the EV powertrain, and the urban driving range of the EV (Table 2) now are projected for every year from 1994 to 2015, with eq. B-1 above. Table VI shows the values of MV, BV, and k in equation B-1, for these parameters. The values assumed are based on a review of recent literature, summarized in Table VII. Note that the driving range is projected to increase as the specific energy and performance of the EV improve.

Range and fuel storage of heavy-duty vehicles

As mentioned above, the driving range of a vehicle, combined with the lb-storage/lb-fuel characteristic of the fuel-storage system, determines the weight of the fuel storage system, which in turn affects the efficiency and hence greenhouse-gas emissions of the vehicle. The weight of the fuel-storage system also directly determines greenhouse-gas emissions from the manufacture of materials for the storage system.

In the original model, I assumed that alternative-fuel HDVs had shorter driving ranges than did the baseline diesel-fuel HDV, and that methanol and ethanol HDVs weighed the same as their diesel counterparts (Table 2). In the present model, I have increased the driving range of all of the alternative-fuel HDVs, to make it closer to that of the diesel baseline, on the assumption that most operators of HDVs want to minimize "down time" spent refueling. I also have increased the lb-storage-system/lb-fuel-weight characteristic of some of the storage systems. (I probably underestimated it before.) Together, of course, these two changes increase the weight of fuel-storage systems on alternative-fuel HDVs, and hence reduce efficiency and increase GHG emissions. The new assumptions are shown in Table VIII. Note that the extra weight of the methanol and ethanol HDV now is calculated, and not just assumed to be zero. The resulting calculated weights are consistent with those reported for transit buses by the National Renewable Energy Laboratory (NREL, 1996).

Soy diesel vehicles: range, fuel storage, and energy use

Soydiesel has been added as a fuel for heavy-duty vehicles. I assume that the tanks and engines for soydiesel are the same as those for diesel fuel (see assumptions in Table VIII). However, on the basis of a few studies discussed in the appendix to this report, I assume that soydiesel is less efficient than diesel fuel, in the base year of 1995. Table IV shows my assumptions regarding the relative efficiency of soy diesel.

Vehicle weight

1). I corrected a minor mistake in the calculation of vehicular curb weight versus loaded weight (Table 2). The curb weight still is calculated on the basis of a statistical relationship between combined city/highway mpg, and vehicle weight.

2). I have added as a new parameter the relative weight of the AFV powertrain and body, so that the user may model the effect on efficiency and hence emissions of assuming a lighter or heavier EV body or powertrain. In the base-case, however, I assume that all ICEV powertrains and bodies weight the same (Table VIII).

3). The model now contains a "weight-compounding" factor, which adds or subtracts weight from the vehicle chassis and suspension as needed according to the difference in weight between the AFV and the baseline ICEV. I assume that every pound of extra weight (in the powertrain, fuel-storage system, or body) requires 0.065 pounds of additional chassis and suspension weight. This parameter makes the treatment of changes in vehicle weight more realistic.

High-pressure hydrogen storage

The two hydrogen storage options now are compressed gaseous hydrogen and liquefied hydrogen, rather than metal-hydride storage and liquefied hydrogen. I have replaced metal-hydride storage with high-pressure hydrogen storage because the latter is much more promising than the former. The high-pressure hydrogen storage system has 25 lbs of storage system per lb. of fuel (estimate based on data in DeLuchi, 1992) (Table VIII).

Choice of LNG or CNG and LH2 or CH2

The process of modeling liquefied natural gas (LNG) and liquefied hydrogen (LH2) has been reduced to a single toggle. Before, in order to switch from CNG (compressed natural gas) to LNG, or CH2 (compressed hydrogen) to LH2, one had to change several parameters values, and copy data from one column to another, throughout the model. Now, one specifies a set of input data (once), and switches between CNG and LNG and CH2 and LH2 with a single toggle.

Materials in vehicles

I changed the estimate of the breakdown of materials that will be used in automobiles in the year 2000, on the basis of trends over the past decade towards greater use of plastics and aluminum (Table P.4) (Motor Vehicle Manufacturer's Association, 1992).

I made some very minor adjustments to the breakdown of energy used to make materials (Table P.4), so that the shares total to 1.00 exactly.

Also, all of the calculations of emissions from the assembly of vehicles and the manufacture of materials have been made more uniform, and easier to follow.

Finally, note that the model does not estimate any emissions associated with recycling.

Materials in electric-vehicle batteries

The model now has materials breakdowns for advanced Pb/acid, nickel-metal/hydride, and lithium/polymer batteries, instead of a breakdown for a sodium/sulfur battery (Table P.5). The breakdowns are shown in Table IX.

I assume that in the early years of the projection period of 1994 to 2015, only Pb/acid batteries are available. Then, a few years later, nickel-metal/hydride batteries are offered, and gradually take over market share from Pb/acid. Lithium/polymer batteries are the last to be offered, but gradually build up market share at the expense of Pb/acid and nickel-metal hydride. Table IX shows the assumed distribution of the total mass of new battery production across the three different battery types, in each year. These shares are used to calculate composite or weighted-average emissions from battery production in a target year.

With the new battery types, four new materials were added to the model: sulfuric acid, potassium hydroxide, nickel, and lithium (Table P.3).

Note that reasonable variation in the materials breakdown can change the g/mi CO2-equivalent emissions by 5.0 or more. It thus is important to specify the battery composition accurately.

Lifetime of HDVs