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Fuel economy and regulated emissions were measured from eight forty-foot transit buses operated on petroleum diesel and a “B20” blend of 80% diesel fuel and 20% biodiesel by volume. Use of biodiesel is attractive to displace petroleum fuel and reduce an operation's carbon footprint. Usually it is assumed that biodiesel will also reduce particulate matter (PM) emissions relative to those of petroleum diesel. Model years of the vehicles evaluated were newer 2007-08 Gillig low-floor buses, 2005 Gillig Phantom buses, and a 2002 Gillig Phantom bus. Engine technology represented three different emissions standards, and included buses with OEM diesel particulate filters. Each bus was evaluated using two transient speed-time schedules, the Orange County Transit Authority (OCTA) driving schedule which represents moderate speed urban/suburban operation and the Urban Dynamometer Driving Schedule (UDDS) which represents a mix of suburban and higher speed on-highway operation.

Biodiesel may be derived from either plant or animal sources, and is usually employed as a compression ignition fuel in a blend with petroleum diesel (PD). Emissions differences between vehicles operated on biodiesel blends and on diesel have been published previously, but data do not cover the latest engine technologies. Prior studies have shown that biodiesel offers advantages in reducing particulate matter, with either no advantage or a slight disadvantage for oxides of nitrogen emissions. This paper describes a recent study on the emissions impact of two biodiesel blends B20A, made from 20% animal fat (tallow) biodiesel and 80% PD, and B20B, obtained from 20% soybean biodiesel and 80% PD. These blends used the same PD fuel for blending and were contrasted with the same PD fuel as a reference. The research was conducted on a 2007 medium heavy-duty diesel truck (MHDDT), with an engine equipped with Exhaust Gas Recirculation (EGR) and a Diesel Particulate Filter (DPF).

West Virginia University characterized the emissions and fuel economy performance of a 30-foot 2010 transit bus equipped with urea selective catalytic reduction (u-SCR) exhaust aftertreatment. The bus was exercised over speed-time driving schedules representative of both urban and on-highway activity using a chassis dynamometer while the exhaust was routed to a full-scale dilution tunnel with research grade emissions analyzers. The Paris speed-time driving schedule was used to represent slow urban transit bus activity while the Cruise driving schedule was used to represent on-highway activity. Vehicle weights representative of both one-half and empty passenger loading were evaluated. Fuel economy observed during testing with the urban driving schedule was significantly lower (55%) than testing performed with the on-highway driving schedule.

Although diesel engines still power most of the heavy-duty transit buses in the United States, many major cities are also operating fleets where a significant percentage of buses is powered by lean-burn natural gas engines. Emissions from these buses are often expressed in distance-specific units of grams per mile (g/mile) or grams per kilometer (g/km), but the driving cycle or route employed during emissions measurement has a strong influence on the reported results. A driving cycle that demands less energy per unit distance than others results in higher fuel economy and lower distance-specific oxides of nitrogen emissions. In addition to energy per unit distance, the degree to which the driving cycle is transient in nature can also affect emissions.

Transit agencies across the United States operate bus fleets primarily powered by diesel, natural gas, and hybrid drive systems. Passenger loading affects the power demanded from the engine, which in turn affects distance-specific emissions and fuel consumption. Analysis shows that the nature of bus activity, taking into account the idle time, tire rolling resistance, wind drag, and acceleration energy, influences the way in which passenger load impacts emissions. Emissions performance and fuel consumption from diesel and natural gas powered buses were characterized by the West Virginia University (WVU) Transportable Emissions Testing Laboratory. A comparison matrix for all three bus technologies included three common driving cycles (the Braunschweig Cycle, the OCTA Cycle, and the ADEME-RATP Paris Cycle). Each bus was tested at three different passenger loading conditions (empty weight, half weight, and full weight).

Typically ethanol is present in gasoline as a 10% blend by volume (E10), although E15, E85 (51 to 83%), and E0 are also available at selected stations. Numerous studies of tailpipe regulated emissions have been conducted to compare emissions from E10 and E0, and there is a growing body of literature addressing blends of E15 and higher. Isolating the effect of ethanol in a study is philosophically difficult, because the ethanol naturally displaces some hydrocarbons, because the ethanol interacts with the remaining gasoline, and because properties of mixing are often nonlinear. Some studies have used splash blending, simply mixing the ethanol with a reference gasoline to produce a blend for comparison to the reference. Others have used match blending, where the objective is to match selected properties of the blend to properties of a reference gasoline.

Fuel consumption (FC) has always been an important factor in vehicle cost. With the advent of electronically controlled engines, the controller area network (CAN) broadcasts information about engine and vehicle performance, including fuel use. However, the accuracy of the FC estimates is uncertain. In this study, the researchers first compared CAN-broadcasted FC against physically measured fuel use for three different types of trucks, which revealed the inaccuracies of CAN-broadcast fueling estimates. To match precise gravimetric fuel-scale measurements, polynomial models were developed to correct the CAN-broadcasted FC. Lastly, the robustness testing of the correction models was performed. The training cycles in this section included a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. The mean relative differences were reduced noticeably.