Estimating the Effects of Pavement Condition on Vehicle Operating Costs (2012)

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51 In recent years, growing world population and the increased demand for road transportation (with its associated energy requirements that are primarily derived from fossil fuels) have led to the consideration, design, and development of energy-efficient vehicles and processes. To realize improve- ments in energy efficiency (with respect to road transpor- tation), various engineering processes and/or technologies have been developed. Among these are drag-reducing vehicle designs, intelligent vehicle operating technologies (e.g., cruise control), use of alternative energy sources (e.g., electricity), and intelligent transportation systems that facilitate wire- less communication between vehicles and transport infra- structure. These and other technological interventions help reduce vehicle operating costs and fossil energy consumption as well as carbon footprints. An overview of these technolo- gies is provided in Appendix D. While emerging vehicle tech- nologies can play a major role in reducing vehicle operating costs and mitigating negative environmental impacts, their impact on the effect of pavement conditions on vehicle oper- ating costs may not be as substantial. This chapter discusses the potential impact of such emerging vehicle technologies on the effect of pavement conditions on vehicle operating costs. Few definitive conclusions could be reached about the effect of pavement conditions on vehicle operating costs for emerging technologies. Mechanistic models are theoretically formulated to con- sider the main physical parameters and apply basic laws of physics/mechanics. By introducing a calibration factor in these models, the effect of emerging vehicle technologies on vehicle operating costs can be predicted. The following types of emerging technologies are discussed in this section: • New engine and combustion technologies to improve the engine efficiency of vehicles, including engine friction reduc- tion, gasoline direct injection, engine downsizing, variable valve actuation, cylinder deactivation, variable compression ratio, homogeneous charge compression ignition, integrated starter/generator systems, continuously variable trans- mission, automated manual transmission, and six+ speed gearboxes; • Alternative fuels and technologies, including hybrid vehi- cles and vehicles powered by natural gas, vehicles powered by electricity, hydrogen, biodiesel, or ethanol; • Vehicle design, including regenerative braking systems, electric motor drive/assist, lightweight materials, reduc- tion of vehicle aerodynamics, and intelligent transportation systems; • Automatic gear shift for heavy trucks; and • Tire technologies, including tire pressure monitoring sys- tems, tire inner liners, use of nitrogen for filling tires, and low rolling resistance tires. New Engine and Alternative Fuel Technology The change in fuel consumption as a function of change in roughness is calculated using Equations 6.1 and 6.2: % ( . )FC FC FC FC 2 1 2 1 1 6 1 − = − FC P ii i= × =ξ , ( . )1 2 6 2or Where: %FC2-1 = Percentage change in fuel consumption as a function of change in IRI FCi = Fuel consumption due to roughness IRIi x = Fuel-to-power efficiency Pi = The total power required (tractive power caused by IRIi, engine drag, and vehicle accessories) Because new engine technology will have better engine efficiency, the same power will be delivered for lower fuel consumption. Therefore, percentage change in power is a constant for a given roughness change if the fuel-to-power C h a p t e r 6 Applicability to Emerging Technologies

efficiency is assumed to not be affected by roughness. By sub- stituting new and old efficiency in Equation 6.2, the following equation is obtained: %FC P P P P P P old old old ne 2 1 2 1 1 2 1 1 − = × −( ) × = −( ) = ξ ξ ξ w new new P P P FC × −( ) × = − 2 1 1 2 1 6 3 ξ % ( . ) Therefore, if the efficiency is not affected by roughness, the new technology will affect only the absolute value of fuel consumption, but not the contribution of roughness on fuel consumption. However, some of the hardware involved with new technologies might be sensitive to vehicle vibration that would require more maintenance under rougher roads than current technologies. In any case, it is likely that the effect of vibrations on the efficiency is secondary. The following sav- ings in the fuel efficiency were reported: 1. Engine and combustion technologies (US Department of Energy, 2010): a. Gasoline direct injection will increase engine efficiency by up to 12%. b. Engine downsizing and cylinder deactivation will both increase engine efficiency by up to 7.5%. c. Variable valve actuation has the potential of increasing engine efficiency of up to 5%. d. Continuously variable and automated manual trans- missions increase the engine efficiency by up to 6% and 7%, respectively. 2. Alternative fuels and technologies: Figure 6-1 shows the average fuel efficiency for passenger cars and light trucks in the United States with and without new technology (Bureau of Transportation Statistics, 2010; US Depart- ment of Transportation, 2010), and the projected fuel efficiency from 2010 through 2015 (NHTSA, 2009). Vehicle Design Vehicle manufacturers seek to minimize aerodynamics through vehicle design (smoothing vehicle shapes). In the United States, the drag coefficient has generally fallen in the 52 (a) SI Units (b) US Customary Units Source: Bureau of Transportation Statistics (2010) 0 20 40 60 80 100 120 140 160 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 F ue l co ns um pt io n (m L /k m ) Year Current Projected 0 5 10 15 20 25 30 35 40 45 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 F ue l ef fi ci en cy ( m pg ) Year Current Projected Passenger car -- Average US Light truck -- Average US Passenger car - New technology Light truck - New technology Figure 6-1. Current and projected average fuel consumption.

current decade and vehicles have become smaller and more fuel efficient. Typical drag coefficients for current passenger cars range from 0.3 to 0.52 and is expected to range from 0.25 to 0.35 for future passenger cars (US Department of Energy, 2010). Truck Manufacturers Association and the US Department of Energy conducted a 2-year collaborative study to investigate a variety of design improvements that would reduce aerodynamic drag and significantly improve fuel efficiency (US Department of Energy, 2006). The following technologies were identified: • Gap enclosures that reduce aerodynamic drag in the gap between the tractor and trailer, • Side skirts that improve aerodynamics and reduce airflow under the trailer in crosswinds, and • Side mirror designs that reconfigure shape and support systems to reduce aerodynamic drag. When introducing all aerodynamic improvements in one vehicle, the reduction in aerodynamic drag could be as much as 23%. Every 2% reduction in aerodynamic drag will result in a 1% improvement in fuel efficiency. However, these reductions in fuel consumption are believed to be slightly affected by pavement conditions. Automatic Gear Shift for Heavy Trucks A study by SCANIA Inc. reported that automatic gearshift for heavy trucks could save as much as 10% in fuel consump- tion (Lundstrom, 2010). However, these reductions in fuel consumption are believed to be slightly affected by pavement conditions. New Tire Technology Use of tires with lower rolling resistance coefficients than conventional tires will result in less fuel consumption. Equa- tion 6.4 describes the rolling resistance model in the HDM 4 after calibration. The rolling resistance is a function of vehicle characteristics and pavement conditions. Fr CR b Nw CR b M b= × × + × × + ×( )( )2 11 1 12 13 6 42υ ( . ) where: b11 to b13 = A function of the wheel diameter b WD b WD b Nw WD 11 37 12 0 064 13 0 012 2 = = =      . / . / WD = Wheel diameter (mm) Nw = Number of wheels M = Mass of the vehicle (kg) u = Vehicle velocity (m/s) CR1 = Rolling resistance tire factor 1.3 for cross-ply bias 1.0 for radial CR2 = Rolling resistance surface factor = Kcr2[a0 + a1  Tdsp + a2  IRI + a3  DEF] Kcr2 = Calibration factor (Table 6-1) Tdsp = Texture depth from the sand patch method (mm) = 1.02 × MPD + 0.28 MDP = Mean profile depth DEF = Benkelman Beam rebound deflection (mm) IRI = International roughness index (m/km) a0 to a3 = Model coefficient (function of the vehicle mass, surface class and type) (Table 6-2) The effect of new tire technology on fuel and tire con- sumption could be accommodated by modifying some of the tire-related variables in HDM 4 such as b11 through b13 and CR1. Alternatively, limited field tests (e.g., coast down test) could be conducted to estimate the new parameters a0 to a3 for CR2. Sandberg (2007) reported the coefficients for the rolling resistance surface factor (CR2) listed in Table 6-3 for advanced tires. Finally, the coefficients C0tc and/or Ctcte could be affected by new tire technologies. If these coefficients were updated, a new calibration study would need to be conducted. Summary The growing demand for fuel-efficient vehicles has accel- erated the research and development efforts dealing with the use of alternative fuels in vehicle propulsion, combustion and 53 Vehicle Class Kcr2 Medium car 0.5 SUV 0.58 Light truck 0.99 Van 0.67 Articulated truck 1.1 Table 6-1. Calibration factor for rolling resistance force. Surface Type 2500 kg Vehicle Mass > 2500 kg a0 a1 a2 a3 a0 a1 a2 a3 Asphalt 0.9 0.022 0.022 0 0.84 0.03 0.03 1.34 Concrete 0.9 0.022 0.022 0 0.84 0.03 0.03 0 Table 6-2. Parameters for rolling resistance surface factor (CR2) model with conventional tires.

54 propulsion processes, environmental issues, aerodynamic/ pavement friction efficiency, and congestion impacts. The technologies presented in this chapter (and Appendix D) have the potential of lowering vehicle operating costs. The major- ity of current research and development efforts that focus on engine and combustion technologies (including alternative fuels) have the potential for significantly reducing energy loss from vehicle operation. The cost of retrofitting existing fleets and the expected decreasing cost of these vehicles will deter- mine the validity of the predicted VOC savings. In summary, new technologies dealing with engines and combustion, alternative fuels, vehicle design and mainte- nance, and tires will affect vehicle operating costs. The effect of pavement conditions on vehicle operating costs will also be influenced by some of these technologies, specifically: 1. New engine technology: The HDM 4 model could be updated by changing the engine efficiency of vehicles to take into account these technologies. This study reports that new engine technologies will increase engine efficiency by 5% to 12% and concludes that the effect of roughness on fuel consumption would likely be unaffected by these technologies. 2. Vehicle design: The HDM 4 model could be updated by changing the aerodynamic characteristics of vehicles to take into account these technologies. This study reports that, when introducing all aerodynamic improvements in one vehicle, the reduction in aerodynamic drag could be as much as 23%. Every 2% reduction in aerodynamic drag will result in a 1% improvement in fuel efficiency. How- ever, these reductions in fuel consumption are believed to be slightly affected by pavement conditions. 3. Automatic gear shift for heavy trucks could save as much as 10% in fuel consumption. However, these reductions in fuel consumption are believed to be slightly affected by pavement conditions. 4. New tire technology: The effect of new tire technology on fuel and tire consumption could be accommodated by modifying some of the tire-related variables in HDM 4 such as b11 through b13 and CR1. Alternatively, limited field tests (e.g., coast down test) could be conducted to estimate the new parameters a0 to a3 for CR2. Although the new technologies will make vehicles more fuel efficient, the expenses of these technologies relative to current vehicles will be higher. Some of the hardware involved with new technologies might be sensitive to vehicle vibra- tion such that more maintenance would be required under rougher roads than current technologies. On the other hand, newer technologies in suspension systems, axle designs, etc. could require less frequent maintenance. In either case, the mechanistic–empirical approach for repair and maintenance costs adopted in this study offers a methodology to further investigate this issue. Surface Type 2500 kg Vehicle Mass > 2500 kg a0 a1 a2 a3 a0 a1 a2 a3 Asphalt 0.5 0.02 0.1 0 0.57 0.04 0.04 0 Concrete 0.5 0.02 0.1 0 0.57 0.04 0.04 0 Source: Sandberg (2007) Table 6-3. New parameters for rolling resistance surface factor (CR2) model with advanced tires.