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Diesel-fueled heavy-duty vehicles (HDVs) can be retrofitted with conversion kits to operate as dual-fuel vehicles in which partial diesel usage is offset by a gaseous fuel such as compressed natural gas (CNG). The main purpose of installing such a conversion kit is to reduce the operating cost of HDVs. Additionally, replacing diesel partially with a low-carbon fuel such as CNG can potentially lead to lower carbon dioxide (CO2) emissions in the tail-pipe. The main issue of CNG-diesel dual-fuel vehicles is the methane (CH4, the primary component of CNG) slip. CH4 is difficult to oxidize in the exhaust after-treatment (EAT) system and its slip may offset the advantage of lower CO2 emissions of natural gas combustion as CH4 is a strong greenhouse gas (GHG). The objective of this study is to compare the emissions of an HDV with a CNG conversion kit operating in diesel and dual-fuel mode during highway operation.

In 2017 the National Research Council of Canada developed an evaporation model for controlling engine icing tunnels in real time. The model included simplifications to allow it to update the control system once per second, including the assumption of sea level pressure in some calculations. Recently the engine icing system was required in an altitude facility requiring operation down to static temperatures of -40°C, and up to an altitude of 9.1 km (30 kft) or 30 kPa. To accommodate the larger temperature and pressure range the model was modified by removing the assumption of sea level operation and expanding the temperature range. In addition, due to the higher concentration of water vapor that can be held by the atmosphere at lower pressures, the significance of the effect of humidity on the air properties and the effect on the model was investigated.

The performance of low-adhesion surfaces in a realistic, in-flight icing environment with supercooled liquid droplets is evaluated using a NACA 0018 airfoil in the National Research Council of Canada Altitude Icing Wind Tunnel. This project was completed in collaboration with McGill University, the University of Toronto and the NRC Aerospace Manufacturing Technologies Centre in March 2022. Each collaborator used significantly different methods to produce low-adhesion surface treatments. The goal of the research program was to demonstrate if the low-adhesion surfaces reduced the energy required to de-ice or anti-ice an airfoil in an in-flight icing environment. Each collaborator had been developing their own low-adhesion surfaces, using bench tests in cold rooms and a spin rig in the wind tunnel to evaluate their performance. The most promising surface treatments were selected for testing on the airfoil.

This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite economic and environmental benefits in freight transportation, lateral stability is always a concern for AHVs in high-speed highway operations due to their multi-unit vehicle structures, and high centers of gravity (CGs). In addition, North American harsh winter weather makes the lateral stability even more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in high-speed evasive maneuvers. AHVs represent a 7.5 times higher risk than passenger cars in highway operation. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs.

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles.

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

Use of natural gas-diesel dual-fuel (NDDF) combustion in compression ignition engines is a method of reducing the net greenhouse gas (GHG) and particulate matter (PM) emissions of these engines. Compressed natural gas (NG) is injected into the intake manifold of the engine and the air-NG mixture is ignited by a direct injection of diesel in the cylinder. One of the main challenges with NDDF combustion is the methane (primary component of NG) slip at low and medium loads, which reduces the engine efficiency and offsets the advantage of lower carbon dioxide emissions of the NG combustion. In order to address this issue, an intake manifold insert is devised with the objective to alter the intake flow profile into the engine and ultimately reduce the methane slip. This is a novel strategy for an NDDF engine since modifying the in-cylinder flow profile can intensify the mixing between diesel and air-NG mixture in order to improve the NG utilization in the cylinder.

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.

This paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone “interlocking” at each junction location deconflicts railway traffic, affirming safe passage.

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver.

In North America, heavy-duty diesel engines for on-road use have to meet strict regulations for their emissions of nitric oxide and nitrogen dioxide (cumulatively referred to as ‘NOx’) besides other criteria pollutants. Over the next decade, regulations for NOx emissions are expected to becoming more stringent in North America. One of the major technical barriers for achieving in-use NOx emissions commensurate with the levels determined from in-laboratory test procedures required by regulations is controlling NOx emissions during cold start and engine idling. Since the exhaust gas temperature can be low during these conditions, the effectiveness of the exhaust after-treatment (EAT) system may be reduced. Under colder climate conditions like in Canada, the impact may be even more significant.

Conventional diesel engines will continue to hold a vital role in the heavy- and medium-duty markets for the transportation of goods along with many other uses. The ability to offset traditional diesel fuels with low-net-carbon biofuels could have a significant impact on reducing the carbon footprint of these vehicles. A prior study screened several hundred candidate biofuel blendstocks based on required diesel blendstock properties and identified 12 as the most promising. Eight representative biofuel blendstocks were blended at a 30% volumetric concentration with EPA certification ultra-low-sulfur diesel (ULSD) and were investigated for emissions and fuel efficiency performance. This study used a single cylinder engine (based on the Ford 6.7L engine) using Conventional Diesel Combustion (CDC), also known as Mixing Control Compression Ignition (MCCI). The density, cetane number, distillation curve and sooting tendency (using the yield sooting index method) of the fuels were measured.

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data.

Natural gas (NG)-diesel dual-fuel combustion can be a suitable solution to reduce the overall CO2 emissions of heavy-duty vehicles using diesel engines. One configuration of such a dual-fuel engine can be port injection of NG to form a combustible air-NG mixture in the cylinder. This mixture is then ignited by a direct injection of diesel. Other potential advantages of such an engine include the flexibility of switching back to diesel-only mode, reduced hardware development costs and lower soot emissions. However, the trade-off is lower brake thermal efficiency (BTE) and higher hydrocarbon emissions, especially methane, at low load and/or high engine speed conditions. Advancing the diesel injection timing tends to improve the BTE but may cause the NOx emissions to increase.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

There has been an increasing interest in exploring the potential to reduce energy consumption of future connected and automated vehicles. People have extensively studied various eco-driving implementations that leverage preview information provided by on-board sensors and connectivity, as well as the control authority enabled by automation. Quantitative real-world evaluation of eco-driving benefits is a challenging task. The standard regulatory driving cycles used for measuring exhaust emissions and fuel economy are not truly representative of real-world driving, nor for capturing how connectivity and automation might influence driving trajectories. To adequately consider real-world driving behavior and potential “off-cycle” impacts, this paper presents four collaborative evaluation methods: large-scale simulation, in-depth simulation, vehicle-in-the-loop testing, and vehicle road testing.

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts.

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application.

The SAE G-27 committee was tasked by ICAO to develop a performance-based packaging standard for lithium batteries transported as cargo on aircraft. The standard details test criteria to qualify packages of lithium batteries & cells for transportation as cargo on-board passenger aircraft. Lithium batteries and cells have been prohibited from shipment as cargo on passenger aircraft since 2016. This paper summarizes the results of the tests conducted by Transport Canada and National Research Council Canada to support the development of this standard with evidence-based recommendations. It includes a description of the test specimens, the test set up, instrumentation used, and test procedures following the standard as drafted to date. The study considered several lithium-ion battery and cell chemistries that were tested under various proposed testing scenarios in the draft standard.

The National Research Council of Canada (NRC) has undergone the development of a Small Axial Compressor Rig for modelling altitude ice accretion in aircraft engines. The rig consists of two axial compressor stages measuring approximately 150mm in diameter, an extension duct to allow residence time for partial melting of ice crystals and a test piece. The axial compressor stages are intended to provide realistic engine conditioning such as fracture, pressure rise, temperature rise and centrifuging of glaciated ice crystals entering the rig. The rig was designed for use in altitude icing wind tunnels such as the NRC’s altitude icing wind tunnel (AIWT), research altitude test facility (RATFac.), and those of other organization such as NASA Glenn and Technical University of Braunshweig. Previous development work [1] provided partial validation of the aerodynamic performance of just the first compressor stage at 90% power.