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The objective of this research project was to compare B20 (20% biodiesel fuel) and ultra-low-sulfur (ULSD) diesel-fueled buses in terms of fuel economy, vehicle maintenance, engine performance, component wear, and lube oil performance. We examined 15 model year (MY) 2002 Gillig 40-foot transit buses equipped with MY 2002 Cummins ISM engines. The engines met 2004 U.S. emission standards and employed exhaust gas recirculation (EGR). For 18 months, eight of these buses operated exclusively on B20 and seven operated exclusively on ULSD. The B20 and ULSD study groups operated from different depots of the St. Louis (Missouri) Metro, with bus routes matched for duty cycle parity. The B20- and ULSD-fueled buses exhibited comparable fuel economy, reliability (as measured by miles between road calls), and total maintenance costs. Engine and fuel system maintenance costs were also the same for the two groups after correcting for the higher average mileage of the B20 group.

Battery electric vehicles possess great potential for decreasing lifecycle costs in medium-duty applications, a market segment currently dominated by internal combustion technology. Characterized by frequent repetition of similar routes and daily return to a central depot, medium-duty vocations are well positioned to leverage the low operating costs of battery electric vehicles. Unfortunately, the range limitation of commercially available battery electric vehicles acts as a barrier to widespread adoption. This paper describes the National Renewable Energy Laboratory's collaboration with the U.S. Department of Energy and industry partners to analyze the use of small hydrogen fuel-cell stacks to extend the range of battery electric vehicles as a means of improving utility, and presumably, increasing market adoption.

Annual fuel use for sleeper cab truck rest period idling is estimated at 667 million gallons in the United States, or 6.8% of long-haul truck fuel use. Truck idling during a rest period represents zero freight efficiency and is largely done to supply accessory power for climate conditioning of the cab. The National Renewable Energy Laboratory’s CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck thermal management systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In addition, if the fuel savings provide a one- to three-year payback period, fleet owners will be economically motivated to incorporate them.

The objective of this research project was to compare the emissions of oxides of nitrogen (NOx) from transit buses on as many as five different fuels and three standard transit duty cycles to establish if there is a real-world biodiesel NOx increase for transit bus duty cycles and engine calibrations. Prior studies have shown that B20 can cause a small but significant increase in NOx emissions for some engines and duty cycles. Six buses spanning engine build years 1998 to 2011 were tested on the National Renewable Energy Laboratory's Renewable Fuels and Lubricants research laboratory's heavy-duty chassis dynamometer with certification diesel, certification B20 blend, low aromatic [California Air Resources Board (CARB)] diesel, low aromatic B20 blend, and B100 fuels over the Manhattan, Orange County and UDDS test cycles.

For renewable fuels to displace petroleum, they must be compatible with emissions control devices. Pure biodiesel contains up to 5 ppm Na + K and 5 ppm Ca + Mg metals, which have the potential to degrade diesel emissions control systems. This study aims to address these concerns, identify deactivation mechanisms, and determine if a lower limit is needed. Accelerated aging of a production exhaust system was conducted on an engine test stand over 1001 h using 20% biodiesel blended into ultra-low sulfur diesel (B20) doped with 14 ppm Na. This Na level is equivalent to exposure to Na at the uppermost expected B100 value in a B20 blend for the system full-useful life. During the study, NOx emissions exceeded the engine certification limit of 0.33 g/bhp-hr before the 435,000-mile requirement.

The future worldwide emission regulations will request a drastic decrease of Diesel engine tailpipe emissions. Depending on the planned application and the real official regulations, a further strong decrease of engine out emissions is necessary, even though the utilized exhaust after-treatment systems are very powerful. To reduce NOx emissions internally, the external exhaust gas recirculation (EGR) is known as the most effective way. Due to the continuously increasing requirements regarding specific power, dynamic behavior and low emissions, future air path systems have to fulfill higher requirements and, consequently, become more and more complex, e.g. arrangements with a 2-stage turbo charging or 2-stage EGR system with different stages of cooling performance.

Emissions from 10 refuse trucks equipped with Caterpillar C-10 engines were measured on West Virginia University's (WVU) Transportable Emissions Laboratory in Riverside, California. The engines all used a commercially available Dual-Fuel™ natural gas (DFNG) system supplied by Clean Air Partners Inc. (CAP), and some were also equipped with catalyzed particulate filters (CPFs), also from CAP. The DFNG system introduces natural gas with the intake air and then ignites the gas with a small injection of diesel fuel directly into the cylinder to initiate combustion. Emissions were measured over a modified version of a test cycle (the William H. Martin cycle) previously developed by WVU. The cycle attempts to duplicate a typical curbside refuse collection truck and includes three modes: highway-to-landfill delivery, curbside collection, and compaction. Emissions were compared to similar trucks that used Caterpillar C-10 diesels equipped with Engelhard's DPX catalyzed particulate filters.

Fuel properties are always considered as one of the main factors to diesel engines concerning performance and emission discussions. There are still challenges for researchers to identify the most correlating and non-correlating fuel properties and their effects on engine behavior. Statistical analyses have been applied in this study to derive the most un-correlating properties. In parallel, sensitivity analysis was performed for the fuel properties as well as to the emission and performance of the engine. On one hand, two different analyses were implemented; one with consideration of both, non-aromatic and aromatic fuels, and the other were performed separately for each individual fuel group. The results offer a different influence on each type of analysis. Finally, by considering both methods, most common correlating and non-correlating properties have been derived.

Chassis dynamometer testing of two 60 foot articulated transit busses, one conventional and one hybrid, was conducted at the National Renewable Energy Laboratory's, ReFUEL facility. Both test vehicles were 2004 New Flyer busses powered by Caterpillar C9 8.8L engines, with the hybrid vehicle incorporating a GM-Allison advanced hybrid electric drivetrain. Both vehicles also incorporated an oxidizing diesel particulate filter. The fuel economy and emissions benefits of the hybrid vehicle were evaluated over four driving cycles; Central Business District (CBD), Orange County (OCTA), Manhattan (MAN) and a custom test cycle developed from in-use data of the King County Metro (KCM) fleet operation. The hybrid vehicle demonstrated the highest improvement in fuel economy (mpg basis) over the low speed, heavy stop-and-go driving conditions of the Manhattan test cycle (74.6%) followed by the OCTA (50.6%), CBD (48.3%) and KCM (30.3%).

Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses a Monte Carlo method to generate parameter sets that are fed to a variant of the road load equation. The modeled road load is then compared to the measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters.

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other three as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method.

NREL completed a temporal and geospatial analysis of telematics data to estimate the fraction of platoonable miles traveled by class 8 tractor trailers currently in operation. This paper discusses the value and limitations of very large but low time-resolution data sets, and the fuel consumption reduction opportunities from large scale adoption of platooning technology for class 8 highway vehicles in the US based on telematics data. The telematics data set consist of about 57,000 unique vehicles traveling over 210 million miles combined during a two-week period. 75% of the total fuel consumption result from vehicles operating in top gear, suggesting heavy highway utilization. The data is at a one-hour resolution, resulting in a significant fraction of data be uncategorizable, yet significant value can still be extracted from the remaining data. Multiple analysis methods to estimate platoonable miles are discussed.

The National Renewable Energy Laboratory's (NREL's) Fleet Test and Evaluations team recently conducted chassis dynamometer tests of a class 8 conventional regional delivery truck over the Heavy Heavy-Duty Diesel Truck (HHDDT), West Virginia University City (WVU City), and Composite International Truck Local and Commuter Cycle (CILCC) drive cycles. A quantitative study analyzed the impacts of various factors on fuel consumption (FC) and fuel economy (FE) by modeling and simulating the truck using NREL's Future Automotive Systems Technology Simulator (FASTSim). Factors included vehicle weight and the coefficients of rolling resistance and aerodynamic drag. Simulation results from a single parametric study revealed that FC was approximately a linear function of the weight, coefficient of aerodynamic drag, and rolling resistance over various drive cycles.

Annual fuel use for long-haul truck rest period idling is estimated at 667 million gallons in the United States. The U.S. Department of Energy's National Renewable Energy Laboratory's CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck climate control systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In order for candidate idle reduction technologies to be implemented at the original equipment manufacturer and fleet level, their effectiveness must be quantified. To address this need, a number of promising candidate technologies were evaluated through experimentation and modeling to determine their effectiveness in reducing rest period HVAC loads.

Emissions from heavy-duty vehicles may be reduced through the introduction of clean diesel formulations, and through the use of catalyzed particulate matter filters that can enjoy increased longevity and performance if ultra-low sulfur diesel is used. Twenty over-the-road tractors with Detroit Diesel Series 60 engines were selected for this study. Five trucks were operated on California (CA) specification diesel (CARB), five were operated on ARCO (now BP Amoco) EC diesel (ECD), five were operated on ARCO ECD with a Johnson-Matthey Continuously Regenerating Technology (CRT) filter and five were operated on ARCO ECD with an Engelhard Diesel Particulate Filter (DPX). The truck emissions were characterized using a transportable chassis dynamometer, full-scale dilution tunnel, research grade gas analyzers and filters for particulate matter (PM) mass collection. Two test schedules, the 5 mile route and the city-suburban (heavy vehicle) route (CSR), were employed.

Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate.