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The main objective of this study is to reduce the aerodynamic drag of tractor-trailer combinations used in the forest industry. In most cases, logging trucks on their return trips are usually travelling in unloaded conditions with upright stakes, which add drag. CFD and wind tunnel testing suggested a drag reduction of up to 35% with no upright stakes, which corresponds to 17% in fuel savings in unloaded conditions. One of the proposed fuel reduction concepts was therefore to have foldable stakes so that the stakes could fold down into a horizontal position while travelling in unloaded conditions. Fuel savings of 15% for a vehicle with stakes in the horizontal position were confirmed with track testing when compared to the fuel consumption of a vehicle with stakes in the vertical position. The coastdown test indicated 28% reduction in drag. The difference in drag reduction between the coastdown test and initial simulation was due to stake size and profile.

Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption – and related emissions – while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning.

In an effort to better understand the operational requirements of port drayage vehicles and their potential for adoption of advanced technologies, National Renewable Energy Laboratory (NREL) researchers collected over 36,000 miles of in-use duty cycle data from 30 Class 8 drayage trucks operating at the Port of Long Beach and Port of Los Angeles in Southern California. These data include 1-Hz global positioning system location and SAE J1939 high-speed controller area network information. Researchers processed the data through NREL’s Drive-Cycle Rapid Investigation, Visualization, and Evaluation tool to examine vehicle kinematic and dynamic patterns across the spectrum of operations. Using the k-medoids clustering method, a repeatable and quantitative process for multi-mode drive cycle segmentation, the analysis led to the creation of multiple drive cycles representing four distinct modes of operation that can be used independently or in combination.

The purpose of this study was to evaluate automatic transmissions in a forestry context by comparing their performance with that of standard manual transmissions, and assessing the possibility of improving fuel efficiency by adapting the engine and automatic transmission performances to the vehicle's load. Long-haul test results showed that during the test day, the degradation in driver performance with the manual transmission truck translated into a 2.9% relative increase in fuel consumption when compared with the automatic transmission truck. The fleet data assessment indicated no obvious difference in fuel consumption between the performance of automatic transmissions and manual transmissions. One system for adapting engine performance to vehicle load uses an onboard weigh scale to determine the load status of the vehicle.

Many trucking fleets and organizations are extensively using truck onboard computers (OBC) to gather fuel consumption data from truck engines' Electronic Control Modules (ECM). This study aimed to assess the accuracy and the precision of truck engine control module concerning the fuel consumption data. The testing methodology evaluated the fuel consumption data provided by the ECM using test track and road fuel consumption tests, short-term operational observation, long-term operational observation and engine dynamometer tests. ECM data were retrieved using either onboard computers (OBC) or engine scan tools. Test track and road tests were mainly intended to evaluate the precision of ECM data for short distances, between 60 and 100 km. More than 220 test runs totalizing 22,000 km were conducted using 23 test vehicles.

Air resistance, after gross vehicle weight, is the largest factor responsible for vehicle energy loss and has an important influence on fuel consumption. The magnitude of aerodynamic drag is affected by the vehicle's shape, frontal area, and travel speed. This study aimed to evaluate several aerodynamic drag reduction measures applicable to class 8 tractor-trailer combinations. The tested aerodynamic devices included trailer aft body rear deflectors (boat tails), trailer skirts, gap deflectors, fuel tank fairings and truck rear-axle fenders. It also assessed the aerodynamic influence of opened doors on an empty wood chip van trailer on the fuel consumption of the tractor-trailer combination. The tests were conducted according to SAE J1321 Joint TMC/SAE Fuel Consumption Test Procedure - Type II.

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.

The objectives of this project were to evaluate the reduction in fuel consumption and greenhouse gas (GHG) emissions made possible by hybrid technology, and to identify good driving habits with this type of vehicle. Two diesel-electric hybrid pick-up and delivery trucks and one diesel-electric hybrid utility vehicle equipped with an electric driven PTO (power take-off) system were included in the project. The first phase was the evaluation in actual operating conditions. Onboard computers were installed in the vehicles to record parameters that make it possible to determine driving habits. Based on operational data, specific duty cycles were built and track tests were conducted to measure the fuel consumption on these duty cycles. It was therefore possible to compare the hybrid trucks with other diesel trucks featuring similar characteristics. The delivery hybrid trucks showed up to 34% fuel savings during the track tests.

In this paper, researchers at the National Renewable Energy Laboratory present the results of simulation studies to evaluate potential fuel savings as a result of improvements to vehicle rolling resistance, coefficient of drag, and vehicle weight as well as hybridization for four powertrains for medium-duty parcel delivery vehicles. The vehicles will be modeled and simulated over 1,290 real-world driving trips to determine the fuel savings potential based on improvements to each technology and to identify best use cases for each platform. The results of impacts of new technologies on fuel saving will be presented, and the most favorable driving routes on which to adopt them will be explored.

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.

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the thermal control/heat rejection system sensitivity to intentional lateral offsets over a range of intervehicle spacings. Previous studies have shown the following vehicle can experience elevated temperatures and reduced airflow through the cooling package as a result of close-formation platooning. Four anemometers positioned across the grille of the following trucks as well as aligned and multiple offset positions are used to evaluate the sensitivity of the impact. Straight sections of the track are isolated for the most accurate airflow impact measurements and to be most representative of on-highway driving. An intentional lateral offset in truck platooning is considered as a controls approach to mitigate reduced cooling efficacy at close following scenarios where the highest platoon savings are achieved.

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified.

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS).

The objective of the project was to compare the fuel consumption of a prototype hybrid electric CNG truck with that of two trucks: a CNG truck and a diesel truck for the similar market and operating conditions. The tests were conducted on a test route representative of the conditions encountered by these vehicles in normal driving operations. The test route length was 276 km with a maximum altitude difference of 374 m. The test route had four sections, including a hilly section with a length of 88 km. The result of the comparison between the two CNG trucks was expressed as fuel savings of CNG in percentage. The fuel consumption of the diesel truck was accurately measured using the gravimetric method. The hybrid electric CNG truck showed average fuel savings of 3.6% and demonstrated up to 7.7% in savings for the entire trip compared to the CNG truck.