Analysis of potential fuel consumption and emissions reductions from fuel cell auxiliary power units (APUs) in long-haul trucks

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1 Energy 32 (27) Analysis of potential fuel consumption and emissions reductions from fuel cell auxiliary power units (APUs) in long-haul trucks Nicholas Lutsey a, Christie-Joy Brodrick a,b,, Timothy Lipman c a Institute of Transportation Studies, University of California, Davis, One Shields Ave., Davis, CA 95616, USA b James Madison University, Integrated Science and Technology Department, MSC 412, ISAT/CS Building, Harrisonburg, VA 2287, USA c Institute of Transportation Studies, University of California, Berkeley, 215 Bancroft Way, 3rd Floor, Berkeley, CA , USA Received 4 September 25 Abstract The idling of heavy-duty trucks results in additional emissions, fuel consumption, and cost. Small fuel cell auxiliary power units (APUs) (now in development) are promising alternatives to idling the main engine. A solid oxide fuel cell (SOFC) APU is particularly attractive, because in conjunction with a reformer, it could operate on widely available diesel fuel. Because fuel cell APUs may not only reduce environmental impacts, but also reduce operating costs, this application has been cited as an attractive early market niche for fuel cells. Our objective is to determine whether SOFC APUs are likely to soon be economically feasible for those trucks that idle most and what energy and environmental benefits are probable. We estimate the APU market size as a function of APU costs by applying Monte Carlo sampling and net present value (NPV) economic analysis to our ADVISOR-based vehicle fuel consumption model. Emissions and fuel economy benefits estimates are then presented as a function of varying market penetration levels, assuming installation on only new vehicles and removal at the end of the 4-year SOFC lifecycle. With modest economic incentives from government and continuing fuel cell technology improvements, we find that SOFC APUs might be economically feasible for up to 15% of the long-haul truck population (6, trucks) in the United States in the 215 time frame, resulting in a 3% reduction of heavy-duty truck fuel use during overnight idling and a 4% reduction in oxides of nitrogen (NO x ) produced during idling. r 27 Elsevier Ltd. All rights reserved. Keywords: Heavy-duty trucks; Fuel cell; Auxiliary power unit (APU) 1. Introduction Drivers of long-haul trucks regularly rest in their truck cabs and idle their engines when doing so. The truck cabs, equipped with climate control devices and a wide assortment of accessories, serve as functional homes for drivers for most of the year. To power in-cab electric appliances and maintain cab climate in seasonal weather, a truck s main propulsion engine is utilized. These large engines are designed for highway freight-hauling situations. They operate very inefficiently when idling. Corresponding author. Institute of Transportation Studies, University of California, Davis, One Shields Ave., Davis, CA 95616, USA. Fax: address: brodricj@jmu.edu (C.-J. Brodrick). The implications of this truck idling are significant. Analysis of the US Department of Commerce s Vehicle Inventory and Use Survey indicates that approximately 4, long-haul Class 7 and 8 trucks routinely travel over 5 miles from their home base, consuming approximately 685 million gallons of diesel fuel per year during overnight idling [1]. Assuming a higher vehicle population of 5, long-haul vehicles, emissions during overnight idling have been estimated to be 1.9 million tons of carbon dioxide (CO 2 ) and 19, tons of oxides of nitrogen annually [2]. Many regional air quality agencies around the United States have already begun to regulate idling [3], and the Bush Administration s national energy program has targeted heavy-duty truck idling [4]. Heavy-duty truck idling is not just an energy and environmental concern, though. Vehicle idling also represents a fuel cost to operators and accelerates engine wear and tear, shortening /$ - see front matter r 27 Elsevier Ltd. All rights reserved. doi:1.116/j.energy

2 N. Lutsey et al. / Energy 32 (27) engine life and increasing maintenance costs. In cases where idling interferes with driver rest, it presents a safety concern. Several technical solutions to supplant idling are currently available, and others are in development. Addon diesel-fired heaters, cooling systems, diesel auxiliary power units (APUs), and generators are commercially available. However, the market penetration of these technologies has been limited. An increasing number of new trucks are being manufactured to be compatible with electrification (or shore power ), in which trucks at truck stops and rest areas are connected to the electric grid and sometimes to other services, such as the Internet [5]. This technology is receiving a great deal of attention in part because it is commercially available and relatively straightforward to fund, given that it generates benefits in a specific state or region. However, only a portion of idling is conducted at truck stops, and the vehicles that idle the most are likely to idle in diverse locations. A third option, fuel cell APUs, which we investigate here, are portable power sources similar to the current diesel-powered APUs and generators. These APUs would likely be targeted at the large market segment that does not stop exclusively at truck stops and other places with electrical power. Fuel cell APUs supply on-board power to replace the idling of the main engine [6 8]. Solid oxide fuel cells (SOFCs) appear to be the most attractive fuel cell for this application, because they can most readily be made to operate on reformed diesel fuel. The trucking industry has historically been very resistant to a secondary fuel, thus limiting the potential for hydrogen fueled SOFC and PEM fuel cells. The US government and a number of companies are investing considerable funding in developing SOFC APUs [8,9], which are the focus of this study. Fuel cells, and SOFCs in particular, must overcome several obstacles, including high capital costs, in order to become commercially viable in vehicle markets. Past assessments of the markets for idle-reduction devices have been limited by lack of available data on truck fuel consumption at idle [1], which is the main cost associated with idling. Without knowing the cost savings possible from reducing fuel consumption at idle, it becomes difficult to estimate the cost-effectiveness of alternatives. In the best available analysis, Argonne National Laboratory used an informal survey of trucking fleets to quantify the fuel savings benefits of technology alternatives to idling truck engines, including direct-fired heaters, small dieselpowered engines used as APUs, and electrification [11]. A study for the state of New York on truck stop electrification used approximated idling fuel consumption averages to estimate the viability of electrification [12]. The only attempt at evaluating the potential benefits of fuel cell APUs to supplant idling was done by Brodrick et al. [13], relying on approximations and sensitivity analysis to address uncertainty. Truck operations are diverse, and it is not the average vehicle, but those that idle the most that are likely to find SOFC APUs cost-effective. Furthermore, the most frequent idlers are the target of policy makers. In order to focus on those trucks that idle most, it is necessary to conduct a sophisticated analysis that reflects the distribution of truck fuel consumption across the fleet, as opposed to utilizing an average fuel consumption number for the fleet. Accordingly, this paper differs from previous studies in its inclusion of measured fuel consumption data from a representative sample of trucks and in the application of Monte Carlo sampling. A 23 nationwide truck stop survey provided a database of idling duration, idling accessory use, and engine settings for the US heavy-duty truck fleet [14]. By applying Monte Carlo sampling to these variables, we obtained probabilistic distributions (n ¼ 1 trials) that represent the variation in the overall survey database. These data were input to a vehicle simulation model [15], which was a modified version of the ADvanced VehIcle SimulatOR version 22 (ADVISOR) [16], to generate a distribution of truck fuel consumption at idle. The fuel consumption, in combination with cost data, was then used to estimate payback periods. Because the variance in the data was preserved, we are able to examine the payback periods specifically for the vehicles that idle the most. We conducted the analysis for the 215 time frame, a period consistent with the commercialization goals and projects set forth by the US Department of Energy. We assumed factory installation on new trucks that were to be put into long-haul service beginning in The APU life was assumed to be 3 4 years at a maximum of 15, h. This time period coincides with the long-haul service life of many trucks. This paper provides a rigorous, quantitative analysis of the future truck market for SOFC APUs. Utilizing the projected cost and performance parameters, we assess whether SOFCs APUs will likely lead to significant, industry-wide idling fuel-consumption reduction in 215. We also estimate emissions benefits. We do so by applying Monte Carlo sampling and net present value (NPV) economic analysis to our ADVISOR-based vehicle simulation model to quantify payback periods for the newer trucks that idle the most. We calculate the market size for truck SOFC APUs integrated into new trucks and determine the impact that tax incentives may have on market size in the 215 time frame. Based on the market size, we use ADVISOR to estimate the fuel consumption and emissions reductions from fuel cell APUs in Research method This research treats a set of truck operations data probabilistically (i.e., using Monte Carlo sampling) and utilizes a vehicle simulation model to estimate truck fuel consumption. The operations data are primarily from the authors previous interview survey of truck drivers across the nation in 23, and this is the largest data set currently available on truck idling characteristics [14]. The simulation model was the ADVISOR 22 model, which the

3 243 ARTICLE IN PRESS N. Lutsey et al. / Energy 32 (27) authors adapted to have an SOFC APU module. A summary of the SOFC APU modeling, including details on design criteria such as sizing and performance, is presented in a previous paper by the authors [15]. The fuel consumption numbers are assigned cost values, and costs of the SOFC APU are estimated. NPV analysis is then applied to determine the payback period for each of the vehicles in the analysis. Fig. 1 illustrates the methodology employed, and key elements of the analysis method are discussed below. 3. Analysis 3.1. Truck operation input data The truck operations that determine fuel consumption include the engine speed setting at idle, the number and duration of use for on-board accessories, and the duration of idling. Monte Carlo sampling of the data set was employed to generate a representative sample of combinations of the variables. Thus, each of the 1 combinations generated reflects one feasible combination of engine settings, accessory use, and idle duration to simulate in total 1 individual drivers and their trucks. The sampling entailed defining the mean and standard deviation for each variable and generating random numbers from within the distribution of these variables. The distribution appears similar to that of the original survey, but it is mathematically more normally distributed and less irregular. In the 23 survey data set, 365 truck drivers reported values for each of these variables. Idle engine speed, measured in revolutions per minute (rpm) varied substantially [14,17]. This adjustable engine setting has a profound effect on idling fuel consumption and emissions [18 24]. A mean of 866 rpm, standard deviation of 28 rpm, and minimum of 4 rpm were identified [14]. Survey results indicated that average drivers report about 6 h of idling per day, operating for about 29 days per year [14]. The mean and standard deviations for annual idling were 17 and 12 h per year, respectively. Values for accessory loads were not gathered in the 23 survey, but an average of 2.1 kw and a standard deviation of 1.3 kw were taken from pilot survey data reported in Lutsey et al. [15]. Emissions estimates were also generated. Cost-effectiveness (in cost per ton of emissions reduced) is a common requirement for incentives, and the emissions data are used later in the analysis when evaluating the effect of incentive strategies on market penetration. The ADVISOR model utilizes engine rpm and accessory load in torque-engine speed maps to estimate fuel consumption. However, comparable engine maps are not available for emissions during idling conditions at low torque and low rpm [15]. In the absence of the more desirable engine maps for emissions, we use empirical relationships for the emission-to-fuel INPUTS Truck Use Variables Idle Engine Speed (rpm) AnnualIdle Duration (hr/yr) Aver. Access. Power (kw) ADVISOR Vehicle Model (for conventional truck and APU-equipped truck) Net Present Value Economic Analysis NPV = K (Benefits, year 1) (Costs, year 1) NPV 1 = NPV + (1+d) 1 (Benefits, year x) (Costs, year x) NPV x = NPV x 1 + (1+d) x Fig. 1. Schematic overview of Monte Carlo method OUTPUTS Potential Benefits, Payback forapus > Annual IdledFuel(gal/yr) AnnualSOFC Savings (gal/yr) SOFC Payback Period (yr)

4 N. Lutsey et al. / Energy 32 (27) consumption rate (g/gal) from the literature to approximate total emissions per truck. These gram-per-gallon ratios are taken from Pekula et al. [24]. Table 1 summarizes the annual idling duration, engine speed, accessory use, and emissions distributions that were generated. Because of practical real-world constraints, some filters were used to modify the results. Namely, negative values for idling duration and accessory power during idling were changed to zero. Negative values can occur when the standard deviations for variables are high, compared to the mean values. For example, for an average accessory power requirement of 2 kw, with standard deviation of 2 kw, using random normal distribution sampling makes for about 65% of values within one standard deviation, and therefore about 16% will happen to fall below zero. A second adjustment was made for engine speeds. Speeds less than 4 rpm were reselected from the probability distribution until they were at least 4, which was the minimum value reported in our 21 pilot survey [17]. As a result of this filtering of the randomly generated trial inputs, the statistical variables (i.e., mean, median, standard deviation) are changed slightly from the mean and standard deviation values obtained from the surveys. The distributions shown in Table 1 were then input into the ADVISOR model to estimate fuel consumption. Table 1 Mean, standard deviation, and distribution for model inputs (n ¼ 1) Variable Arithmetic mean Standard deviation Distribution Annual idling duration (h/yr) Engine speed at idle (rpm) Annual Idle Duration (hr/yr) Average accessory power during idling period (kw) a Idle Engine Speed (rpm) Emission rate of oxides of nitrogen (g NO x /gal) Aver. Access. Power (kw) 1 5 Emission rate of hydrocarbons (g HC/gal) NOx Emission (g/gal) 3 More HC Emission (g/gal) a These are the average loads for the APU system. The magnitudes and the electric load profiles are different for the engine idling scenario, as discussed in Lutsey et al. [15].

5 2432 ARTICLE IN PRESS N. Lutsey et al. / Energy 32 (27) Fuel consumption modeling The vehicle model is based upon the National Renewable Energy Laboratory s ADvanced VehIcle SimulatOR (ADVISOR 22 edition), a MATLAB/Simulink-based program [16]. The tool was originally designed to model the performance and fuel economy of conventional, electric, hybrid, and fuel cell vehicles. The model allows the user to change components and operation, and then ADVISOR simulates the vehicle s response under different driving conditions. We summarize the most important aspects for our trucks-and-apu-specific adaptation of the ADVISOR model; however, for further details on our enhanced ADVISOR vehicle model, please see the authors previous work, [15]. The original ADVISOR 22 model was first modified to better model accessory power-load profiles and engine conditions for an idling truck and to provide comparisons between existing truck systems and auxiliary power alternatives, such as APUs. Inputs to the model include characteristics of the vehicle duty cycle (e.g., speed vs. time), which are in turn used to calculate characteristics such as load and speed required at each component (e.g., transmission, engine, etc.) and ultimately to estimate fuel consumption and emissions from the vehicle. Key enhancements to the standard ADVISOR program were in the following five areas: (1) creating representative vehicle duty cycles (including idling time) for long-haul trucks, (2) inputting engine-specific fuel and emissions consumption maps, (3) adding and integrating an optional APU module, (4) estimating fuel cell performance data, and (5) determining the appropriate size of the fuel cell APU. A key deficiency of the model is the inability to dynamically model emissions. As described above, in the absence of comprehensive emission engine maps, we estimate emissions of oxides of nitrogen and hydrocarbons by assuming constant emission per unit fuel (g/gal) ratios. The results from the 1 ADVISOR runs for both the engine idling and SOFC APU scenarios are shown in Table 2. The mean and median values were both about.9 gal of diesel per hour, similar to the often-assumed, industry-reported average for fuel consumption during idling. Ninety percent of the values lie between.5 and 1.48 gal/h. Multiplying the output of diesel consumption rate at idle (gal/h) by the annual idling duration (hour per year) for each of the 1 runs generated the annual idled fuel, which averaged about 16 gal per truck per year. Seventy-two of the 1 trials have no annual diesel consumed while at idle. These runs correspond to the roughly 1% of truck stop survey respondents who reportedly idled their main engines during non-driving periods for less than an hour per day. The outputs for the SOFC fuel consumption reveal a noticeably more flat distribution, mainly due to the relatively flat performance curve of the SOFC system, allowing the SOFC system to often operate within its ideal operating zone of between 3% and 7% of the peak load of 5 kw. Along with the 72 (7.2%) of non-idling trials, another 74 trials had near-zero accessory load while still having non-zero idling durations. This 7.4% could represent those trucks that idle unavoidably (e.g., for power-take-off applications) or simply without the goal of supplying accessory power for heating, cooling, or electricity. Together these 15% of trials are all assumed not to be amenable to APUs. Looking at the relevant 85% of the trials, the SOFC APU diesel consumption values reveal a mean and median of.15 gal/h. Replacing engine idling with the fuel cell APU would result in a mean 8% improvement in fuel consumption during the stationary portion of the cycle for all trucks with avoidable idling. The 9% confidence interval for the trials was from a 59% to 94% reduction in idled diesel use. For the approximately 7% of drivers who reportedly do not idle, of course, no gain would result. Likewise, for the 7% of drivers who reportedly idle with little or no accessory loading, no potential benefit is possible. Subtracting total annual diesel consumed from the idling engine from that of the SOFC APU, the potential diesel savings were calculated for each trial. For all trials, the mean was about 12 gal per year (standard deviation of 11); whereas for the 85% of trials with avoidable idling, the mean was about 14 gal per year. Notably, it is the rightmost tail of the potential annual fuel savings distribution that is of most importance here, for it is this subset of the population that has the most to gain from early adoption of idle-reduction technologies. The 9th percentile of potential fuel savers (or 1% of trials with the highest savings) annually save at least 25 gal of diesel, and the 95th percentile saves greater than 34 gal per year Payback estimates The payback period for each truck is evaluated by estimating the cost savings associated with reduced fuel consumption, then subtracting the capital investment. To project paybacks in 215 with benefits in subsequent years, a NPV analysis is employed. An NPV analysis involves an assessment of a current capital investment along with costs and benefits in the future. The period over which the investment breaks even, or when the sum of future benefit equals the initial and future costs, is known as the payback period. In this case, the fuel cell APU, its ancillary components, and its installation costs make up the capital investment. The NPV analysis conducted here includes only the potential direct fuel cost savings to the driver (or company that owns the vehicle). Although there are other potential benefits, such as reduced engine maintenance costs due to reduced idling and the effects of reduced emissions, these are not included in the NPV. Operating and maintenance cost (e.g., oil and lubricant changes) estimates in the literature vary widely and are much less likely to influence investment decisions. Key economic variables assumed here for the NPV analysis are summarized in Table 3. Because distributions

6 N. Lutsey et al. / Energy 32 (27) Table 2 Fuel consumption model outputs (n ¼ 1) Variable Mean Standard deviation Distribution Idle fuel rate (gal/h) SOFC APU fuel rate (gal/h) Idle Fuel Consumption (gal/hr) Fraction of idling fuel saved with SOFC APU a SOFC APU Fuel Cons. (gal/hr) Annual idling fuel use (gal/yr) Fraction of Idled Fuel Saved.9 Annual SOFC fuel use (gal/yr) Annual fuel savings with SOFC APU (gal/yr) Annual SOFC Fuel (gal/yr) Annual SOFC APU Savings (gal/yr) a The 15% (146 of 1) with zero outputs correspond to drivers that reportedly have negligible accessory load power needs and would thereby save nothing with an APU for accessory needs. These zero values are not included in the mean and standard deviation calculation. Table 3 Summary of economic variables for NPV analysis Variable Lower market limit Middle estimate Upper market limit Diesel price $1.5/gal $2./gal $3./gal Discount rate (real) 7% 5% 3% Inverter $14 Heat pump $18 Installation $15 Misc. (housing, conduit, etc.) $5 of these variables are not known, nor are they known to be normally distributed, a range of values is used for each variable. For example, the choice of the real discount rate, or time value of money, of 5% is chosen with low and high estimates of 3% and 7%, respectively. Similarly, the cost of a gallon of diesel at truck stops is varied to incorporate its relatively volatile nature. Using DOE data, the estimates used were a low value of $1.5/gal, a middle value of $2./ gal, and a high value of $3. gal, corresponding approximately to the lowest, average, and highest annual-average U.S. diesel prices from 24 to 27 [25].

7 2434 N. Lutsey et al. / Energy 32 (27) The capital cost of the SOFC system and the ancillary costs of integrating it with the vehicle must be included in the capital investment cost. Lutsey et al. [15] initially used a 4-kW (net) fuel cell. Here, instead, noting the distribution of accessory power values (Fig. 1) and that a new niche market fuel cell would be produced with a more one-sizefits-all approach, we selected a 5-kW (net) SOFC system. Initially, the fuel cell system cost is assumed to be $4/kW including the reformer and other balance of plant components. This cost is in accordance with the US DOE SECA target for 211 [26]. We note that these targets are lower than many present-day estimates for factory costs for SOFC systems. In the sensitivity analysis we therefore consider higher cost cases in case the SECA goals are not achieved (see below). We include the cost of a power inverter so that the APU can provide power for alternating current-based appliances and other onboard loads, although in principle these devices could operate on direct current and the inverter could be replaced with a less expensive DC DC converter. Estimates for the component costs used here are based on previous assessments by the authors [17]. The total APU capital cost, assuming the US DOE target of $4/kW for the 5-kW fuel cell system, is $72. The annual potential fuel savings for each truck are then used to determine the private cost benefit accrued over time with the investment of the SOFC APU for each trial. In Fig. 2, the distribution of trials is shown with the cumulative percent of long-haul trucks that have payback periods at or less than the given time frame. A payback period of 2 years is thought to be a maximum threshold parameter for long-haul truck investment [17]. This analysis suggests that this 2-year payback period for future U.S. DOE-target-priced SOFC APUs could correspond to 2% (with a span of lower to upper estimates from 1% to 35%) of the long-haul truck population. A smaller segment of the truck population, about 1%, is estimated to have payback periods less than or equal to 1.5 years Potential market size as a function of varying solid oxide fuel cell cost In Fig. 3, instead of assuming the US DOE target SOFC cost of $4/kW and calculating the payback period of the investment for each truck, the SOFC cost is varied from a high of $12 down to the low of $4/kW. By computing the cumulative percent of trucks with payback periods of 2 years or less, we estimate the maximum potential size of the SOFC APU market size in the long-haul truck application as fuel cell costs drop over time. Again assuming that there 6 6% (from n=1) Cumulative % 5% 4% 3% 2% 1% Cumulative percent of trucks % Payback period (yr) Fig. 2., cumulative percent of trucks with given payback period for investment in SOFC APU. 4% 16, Percent of Line-Haul Trucks 3% 2% 1% 12, 8, 4, Estimated Number of Trucks % $1,2 $1, $8 $6 $4 SOFC APU Cost ($/kw) Fig. 3. Percent and number of long-haul trucks with 2-year or less payback periods as function of solid oxide fuel cell APU cost.

8 N. Lutsey et al. / Energy 32 (27) are 4, total long-haul trucks, the total potential number of tractor-trailers is estimated. The figure suggests that if the cost of SOFC technology dropped to $8/kW, about 1% of long-haul trucks could adopt the technology with a payback period of 2 years or less. If the US DOE s target of $4/kW is obtained, SOFC APUs could be an economically viable product for about 2% of the truck population, or about 8, trucks Emissions savings The results for idling emissions are shown in Table 4. In order to determine the potential emissions benefit of introducing APUs to reduce idling, we assume that the SOFC has negligible NO x and HC emissions. An average of.3 tons NO x and.8 tons HC per truck may be reduced by the APU annually Potential fuel and emissions savings from highest fuel users Table 5 summarizes the cumulative effect of those vehicles that idle the most (those that have the most potential gain) purchasing the APU systems. These results indicate that a relatively small number of APU purchasers could potentially make a significant reduction in the total Table 4 Emissions model outputs (n ¼ 1) Variable Mean Standard deviation Distribution Annual oxides of nitrogen emissions (ton/yr) Annual hydrocarbon emissions (ton/yr) Annual total oxides of nitrogen and hydrocarbon (NO x +HC) emissions (ton/yr) NOx Emission (ton/yr).16.2 HC Emission (ton/yr) More NOx+HC Emission (ton/yr) Table 5 Cumulative effect of various levels of APU penetration in long-haul trucks Percent of in-use trucks equipped with SOFC APU (%) a Estimated total number of longhaul trucks b Minimum diesel savings (gal/truck-yr) Cumulative diesel savings (% of all idled fuel) (%) Average NO x and HC emissions reduction (ton/truck-yr) Cumulative NO x and HC emissions reduction (% of all idled NO x +HC) (%) , , , , , , , , a The percentage of trucks that have the highest potential savings with a SOFC APU to supplant idling. b Based on US Census 2 Vehicle inventory and use survey data of approximately 4, total long-distance (5-mile or greater range) Class 7 and 8 trucks [1].

9 2436 ARTICLE IN PRESS N. Lutsey et al. / Energy 32 (27) fuel consumption and emissions produced by idling. If those in the top 1th percentile employed APUs, it would reduce total fleet fuel consumption during idling by 23%, and total idling-related NO x and HC emission could be reduced by 32%. Assuming 4, total long-haul trucks, this would equate to about 4, units. Equipping 2% of the heaviest idling long-haul trucks could reduce total idled fuel by about 37% and idled NO x and HC emissions by almost 5% Effect of tax credit for APU purchases Noting that the initial capital cost of the immature technology of SOFCs is a key hurdle to the introduction of fuel cells in any application, we examine the effect of a tax credit to defray the initial purchase cost of the APU. Fig. 4 shows the results of $1 and $2 tax credits for the purchase of a 5-kW SOFC APU (note that a $1 tax credit would be roughly equivalent to a $4 tax deduction, based on a 25% income tax bracket). In the figure, the percent and number of trucks with payback periods less than or equal to 2 years are shown for given SOFC cost, again ranging from $12 to $4/kW of net fuel cell system output. A $1 tax credit for the fuel cell APU purchase increases the potential market by approximately 5%, whereas the $2 tax credit approximately doubles the potential market. At a SOFC cost of $8/kW, the potential SOFC APU market in long-haul trucks is estimated to be about 4, trucks. Tax credits of $1 and $2 would increase this number to approximately 61, and 78, trucks, respectively. The total cost-effectiveness (cost per ton of emissions reduced) associated with the introduction of $1 and $2 tax credits is shown in Table 6. The results assume that the SOFC emissions of NO x and HC are negligible compared to the idling trucks and that SOFC APUs have a lifetime of 3 4 years of emissions reductions. Calculations here are for a SOFC cost of $8/kW, a moderate cost estimate for the time frame. The $1 tax credit, while increasing the total potential market by 5%, does so at a cost of only about $3 per ton of NO x and HC removed. The $2 tax credit, while increasing the Percent of Line-Haul Trucks 5% 4% 3% 2% 1% No tax credit $1, tax credit $2, tax credit 2, 16, 12, 8, 4, Estimated Number of Trucks % $1,2 $1, $8 $6 $4 SOFC APU Cost ($/kw) Fig. 4. Percent and number of long-haul trucks with 2-year or less payback periods as function of solid oxide fuel cell APU cost, with varying financial incentives. Table 6 Cost-effectiveness of APU purchase tax credit on APU market penetration (for SOFC cost of $8/kW) and NO x and HC emissions reduction cost Tax credit on SOFC APU purchase Potential market penetration a Percent of long-haul trucks (%) Total number of longhaul trucks b Average NO x +HC reduction (ton/truck-yr) b Average emission reduction cost ($/ton NO x +HC reduced) b,c None 1 4, 1.3 (5 28) (19, 11,) (.9 1.6) $ , 1.1 $35 (7 32) (27, 126,) (.8 1.5) ($23 $41) $2 2 78, 1. $67 (9 37) (34, 149,) (.7 1.3) ($5 $9) a Potential market penetration is percent and number of trucks with payback periods of less than 2 years for SOFC APU purchase; baseline values are for $2./gal of diesel and d ¼ 5%; parentheses for lower (d ¼ 7%, $1.5/gal) and upper (d ¼ 3%, $3./gal) estimates; b Based on 4, total long-haul trucks. c Assumes APU emission reductions for 3-year period.

10 N. Lutsey et al. / Energy 32 (27) total potential market by 1%, does so at a cost of about $7 per ton of NO x and HC removed. For context, projections for NO x allowance prices suggest that the typical range from $2 to $3 per ton NO x reduced will hold for many years [27]. The results presented are based on data from 23 trucks and fuel prices and are not adjusted for the truck emissions and operations changes that will happen between then and the time frame that we examine. The market is changing rapidly due to a myriad of factors, including idling bans, hours-of-service regulations, new engine emissions regulations, education and outreach efforts, and idling-reduction incentives. There are inadequate data for accurately forecasting the effects of these changes. For example, we do not have quantitative evidence on (1) how new engines will penetrate the truck market and how the engines reduced emissions levels will relate to actual, on-road idling emissions and fuel consumption, (2) how operation patterns will change as truck operators respond to idling bans and hours-of-service limitations, and (3) what idling-reduction financial incentives and idling-reduction alternatives will be offered as well as what the spatial distribution of these may be. Somewhat ironically, the portability of APUs, which is the very characteristic that makes them valuable to the truck market, can be a limitation from a regulatory standpoint. Current incentive funding is often distributed by region and is based on obtaining emissions reductions in certain locations that have poor air quality. Shore power is easily funded under the current structure; however, APUs seldom achieve significant reductions in a particular area. In order to achieve the APU cost-effectiveness estimated in this paper for the cases that include incentives, a less spatially restrictive incentives structure would have to be adopted. 4. Conclusions The long-haul truck market is often cited by regulators and researchers as a potential early niche for relatively high-cost fuel cells; however, this is a quantitative investigation of the market potential and cost-effectiveness of fuel cells in this application. Utilizing Monte Carlo sampling in combination with empirical data on truck operations, we estimate the distribution of idling times, accessory use, and idle duration for the US long-haul truck fleet. These data were input into the ADVISOR simulation model to estimate the fuel consumption for baseline idling trucks and for trucks equipped with solid oxide fuel cell APUs. Combining this with cost data, we are then able to estimate the extent to which the potential SOFC APU market in 215 could achieve reductions in idled fuel consumption. We conclude that as SOFC technology approaches $8 per net kw delivered considerably more conservative than the US DOE 211 $4/kW target a market in the tens of thousands of new long-haul trucks would meet the 2-year-or-less payback periods demanded by the trucking industry. Relatively modest tax incentives of a thousand dollars per unit, with cost-effectiveness ratios of less than $3 per ton of NO x and HC reduced, could lead to market penetration of 15% (6, long-haul trucks) in 215. This market penetration of 6, trucks corresponds to a 3% reduction of fuel use from idling long-haul trucks. The tax credits for NO x and HC reduction via SOFC APUs, could be an order of magnitude more cost effective than much larger scale emission reduction projects. Thus, the use of SOFC technology in long-haul trucks could provide a rare opportunity to simultaneously cut industry operational costs, accelerate the introduction of less mature SOFC technology, and offer cost-effective emission reductions. Acknowledgments The authors would like to thank UC Davis colleague John Wallace for his ADVISOR programming. The National Science Foundation s Integrative Graduate Education and Research Traineeship (IGERT) program provided student funding. Linda Gaines of Argonne National Laboratory provided comments. The US Department of Energy, the California Air Resources Board, and Freightliner LLC have all contributed to our research on trucks and fuel cells. References [1] Gaines L, Vyas A, Anderson J. Estimation of fuel use by idling commercial trucks. Transp Res Rec 26;1983:91 8. [2] Lim H. Study of exhaust emissions from idling heavy-duty diesel trucks and commercially available idle-reducing devices. 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