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Blunt impacts to the back of the torso can occur in vehicle crashes due to interaction with unrestrained occupants, or cargo in frontal crashes, or intrusion in rear crashes, for example. Six pendulum tests were conducted on the back of an instrumented 50th percentile male Hybrid III ATD (Anthropomorphic Test Device) to determine kinematic and biomechanical responses. The impact locations were centered with the top of a 15-cm diameter impactor at the T1 or at T6 level of the thoracic spine. The impact speed varied from 16 to 24 km/h. Two 24 km/h tests were conducted at the T1 level and showed repeatability of setup and ATD responses. The 16 and 24 km/h tests at T1 and T6 were compared. Results indicated greater head rotation, neck extension moments and neck shear forces at T1 level impacts. For example, lower neck extension was 2.6 times and 3.8 times greater at T1 versus T6 impacts at 16 and 24 km/h, respectively.

National concerns over energy consumption and emissions from the transportation sector have prompted regulatory agencies to implement aggressive fuel economy targets for light-duty vehicles through the U.S. National Highway Traffic Safety Administration/Environmental Protection Agency (EPA) Corporate Average Fuel Economy (CAFE) program. Automotive manufacturers have responded by bringing competitive technologies to market that maximize efficiency while meeting or exceeding consumer performance and comfort expectations. In a collaborative effort among Toyota Motor Corporation, Argonne National Laboratory (ANL), and the National Renewable Energy Laboratory (NREL), the real-world savings of one such technology is evaluated. A commercially available Toyota Highlander equipped with two-phase cold storage technology was tested at ANL’s chassis dynamometer testing facility.

We investigate tradeoffs between the airflow strategies related to engine cooling and the aerodynamic-enabled fuel savings created by platooning. By analyzing air temperatures, engine temperatures and cooling air flow at different platoon distances, we show the thermal impact to the engine from truck platooning. Previously, we collected wind and thermal data for numerous heavy-duty truck platoon configurations (gaps ranging from 4 to 87 meters) and reported the significant fuel savings enabled by these configurations. The fuel consumption for all trucks in the platoon were measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate while travelling at 65 mph and loaded to a gross weight of 65,000 lb.

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.

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

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.

DME has been considered an alternative fuel to diesel fuel with promising benefits because of its high reactivity and volatility. Research shows that an engine fueled with DME will produce zero smoke emissions. However, the storage and the handling of the fuel are underlying difficulties owing to its high vapour pressure (530 kPa @ 20 °C). In lieu, OME1 fuel, a derivate of DME, offers advantages exhibited with DME fuel, all the while being a liquid fuel for engine application. In this work, engine tests are performed to realize the combustion behaviour of DME and OME1 fuel on a single-cylinder research engine with a compression ratio of 9.2:1. The dilution ratio of the mixture is progressively increased in two manners, allowing more air in the cylinder and applying exhaust gas recirculation (EGR). The high reactivity of DME suits the capability to be used in compression ignition combustion whereas OME1 must be supplied with a supplemental spark to initiate the combustion.

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.

Previously fuel consumption on a drive cycle has been shown to be proportional to traction work, with an offset for powertrain losses. This model had different transfer functions for different drive cycles, performance levels, and applied powertrain technologies. Following Soltic it is shown that if fuel usage and traction work are both expressed in terms of cycle average power, a wide range of drive cycles collapse to a single transfer function, where cycle average traction power captures the drive cycle and the vehicle size. If this transfer function is then normalized by weight, i.e. by working in cycle average power/weight (P/W), a linear model is obtained where the offset is mainly a function of rated performance and applied technology. A final normalization by rated power/weight as the primary performance metric further collapses the data to express the cycle average fuel power/rated power ratio as a function of cycle average traction power/rated power ratio.

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).

There are a variety of test protocols associated with vehicle fuel economy and emissions testing. As a result, a number of test protocols currently exist to measure axle efficiency and spin loss. The intent of this technical paper is to describe a methodology that uses a singular axle efficiency and spin loss procedure. The data can then be used to predict the effects on vehicle FE and GHG for a specific class of vehicles via simulation. An accelerated break-in method using a comparable energy approach has been developed, and can be used to meet the break-in requirements of different vehicle emission test protocols. A “float to equilibrium” sump temperature approach has been used to produce instantaneous efficiency data, which can be used to more accurately predict vehicle FE and GHG, inclusive of Cold CO2. The “Float to Equilibrium” approach and “Fixed Sump Temperature” approach has been compared and discussed.

This study assesses the exposure distribution and injury rate (MAIS 4+F) to front-outboard non-ejected occupants by crash severity, belt use and head restraint type and damage in rear impacts using 1997-2015 NASS-CDS data. Rear crashes with a delta V <24 km/h (15 mph) accounted for 71% of all exposed occupants. The rate of MAIS 4+F increased with delta V and was higher for unbelted than belted occupants with a rate of 11.7% ± 5.2% and 6.0% ± 1.5% respectively in 48+ km/h (30 mph) delta V. Approximately 12% of front-outboard occupants were in seats equipped with an integral head restraint and 86% were with an adjustable head restraint, irrespective of crash severity. The overall injury rate was 0.14% ± 0.05% and 0.22% ± 0.06%, respectively. It was higher in cases where the head restraint was listed as “damaged”. Thirteen cases involving a lap-shoulder belted occupant in a front-outboard seat in which “damage” to the adjustable head restraint was identified.

The brake pedal is the brake system component that the driver fundamentally has contact and through its action wait the response of the whole system. Each OEM defines during vehicle conceptualization the behavior of brake pedal that characterizes the pedal feel that in general reflects not only the characteristic from that vehicle but also from the entire brand. Technically, the term known as Pedal Feel means the relation between the force applied on the pedal, the pedal travel and the deceleration achieved by the vehicle. Such relation curves are also analyzed in conjunction with objective analysis sheets where the vehicle brake behavior is analyzed in test track considering different deceleration conditions, force and pedal travel. On technical literature, it is possible to find some data and studies considering the hydraulic brakes behavior.

Optimization of heavy materials like steel, in order to create a lighter vehicle, it is a major goal among most automakers, since heavy vehicles simply cannot compete with a lightweight model's fuel economy. Thinking this way, this paper shows a case study where the Size Optimization technique is applied to a front floor reinforcement. The reinforcement is used by two different vehicles, a subcompact and a crossover Sport Utility Vehicle (SUV), increasing the problem complexity. The Size Optimization technique is supported by Finite Element Method (FEM) tools. FEM in Computer Aided Engineering (CAE) is a numerical method for solving engineering problems, and its use can help to optimize prototype utilization and physical testing.

The lack of electrical conductivity on materials, which are used in automotive fuel systems, can lead to electrostatic charges buildup in the components of such systems. This accumulation of energy can reach levels that exceed their capacity to withstand voltage surges, which considerably increases the risk of electrical discharges or sparks. Another important factor to consider is the conductivity of the commercially available fuels, such as biodiesel, which contributes to dissipate these charges to a proper grounding point in automobiles. From 2013, the diesel regulation in Brazil have changed and the levels of sulfur in the composition of diesel were reduced considerably, changing its natural characteristic of promoting electrostatic discharges, becoming more insulating.

The brake pedal is the brake system component that the driver fundamentally has contact and through its action wait the response of the whole system. Each OEM defines during vehicle conceptualization the behavior of brake pedal that characterizes the pedal feel that in general reflects not only the characteristic from that vehicle but also from the entire brand. Technically, the term known as Pedal Feel means the relation between the force applied on the pedal, the pedal travel and the deceleration achieved by the vehicle. Such relation curves are also analyzed in conjunction with objective analysis sheets where the vehicle brake behavior is analyzed in test track considering different deceleration conditions, force and pedal travel. On technical literature, it is possible to find some data and studies considering the hydraulic brakes behavior.

This work explores the impact of the interaction of lubricant and fuel properties on the propensity for stochastic pre-ignition (SPI). Findings are based on statistically significant changes in SPI tendency and magnitude, as determined by measurements of cylinder pressure. Specifically, lubricant detergents, lubricant volatility, fuel volatility, fuel chemical composition, fuel-wall impingement, and engine load were varied to study the physical and chemical effects of fuel-lubricant interactions on SPI tendency. The work illustrates that at low loads, with fuels susceptible to SPI events, lubricant detergent package effects on SPI were non-significant. However, with changes to fuel distillation, fuel-wall impingement, and most importantly engine load, lubricant detergent effects could be observed even at reduced loads This suggests that there is a thermal effect associated with the higher load operation.