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Terrestrial winds play an important role in affecting the aerodynamics of road vehicles. Of increasing importance is the effect of the unsteady turbulence structure of these winds and their influence on the process of optimizing aerodynamic performance to reduce fuel consumption. In an effort to predict better the aerodynamic performance of heavy-duty vehicles and various drag reduction technologies, a study was undertaken to measure the turbulent wind characteristics experienced by heavy-duty vehicles on the road. To measure the winds experienced on the road, a sport utility vehicle (SUV) was outfitted with an array of four fast-response pressure probes that could be arranged in vertical or horizontal rake configurations that provided measurements up to 4.0 m from the ground and spanning a width of 2.4 m. To characterize the influence of the proximity of the vehicle on the pressure signals of the probes, the SUV and its measurements system was calibrated in a large wind tunnel.

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

The authors present transient wind velocity measurements from two successive, well-documented truck platooning track-test campaigns to assess the wake-shedding behavior experienced by trucks in various platoon formations. Utilizing advanced analytics of data from fast-response (100-200-Hz) multi-hole pressure probes, this analysis examines aerodynamic flow features and their relationship to energy savings during close-following platoon formations. Applying Spectral analysis to the wind velocity signals, we identify the frequency content and vortex-shedding behavior from a forward truck trailer, which dominates the flow field encountered by the downstream trucks. The changes in dominant wake-shedding frequencies correlate with changes to the lead and follower truck fuel savings at short separation distances.

An integrated adaptive cruise control (ACC) and cooperative ACC (CACC) was implemented and tested on three heavy-duty tractor-trailer trucks on a closed test track. The first truck was always in ACC mode, and the followers were in CACC mode using wireless vehicle-vehicle communication to augment their radar sensor data to enable safe and accurate vehicle following at short gaps. The fuel consumption for each truck in the CACC string was measured using the SAE J1321 procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb, demonstrating the effects of: inter-vehicle gaps (ranging from 3.0 s or 87 m to 0.14 s or 4 m, covering a much wider range than previously reported tests), cut-in and cut-out maneuvers by other vehicles, speed variations, the use of mismatched vehicles (standard trailers mixed with aerodynamic trailers with boat tails and side skirts), and the presence of a passenger vehicle ahead of the platoon.

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.

A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC).

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 a campaign to quantify the aerodynamic drag changes associated with drag reduction technologies recently introduced for light-duty vehicles, a 3-year, 24-vehicle study was commissioned by Transport Canada. The intent was to evaluate the level of drag reduction associated with each technology as a function of vehicle size class. Drag reduction technologies were evaluated through direct measurements of their aerodynamic performance on full-scale vehicles in the National Research Council Canada (NRC) 9 m Wind Tunnel, which is equipped with a the Ground Effect Simulation System (GESS) composed of a moving belt, wheel rollers and a boundary layer suction system. A total of 24 vehicles equipped with drag reduction technologies were evaluated over three wind tunnel entries, beginning in early 2014 to summer 2015. Testing included 12 sedans, 8 sport utility vehicles, 2 minivans and 2 pick-up trucks.