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In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks (i.e., gross vehicle weight greater than 4,536 kg). The Enhanced Rear Signaling (ERS) for Heavy Trucks project was developed by the Federal Motor Carrier Safety Administration (FMCSA) to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. Visual warnings have been shown to be effective, assuming the following driver is looking directly at the warning display or has his/her eyes drawn to it. A visual warning can be placed where it is needed and it can be designed so that its meaning is nearly unambiguous. FMCSA contracted with the Virginia Tech Transportation Institute (VTTI) to investigate potential benefit of additional rear warning-light configurations as rear-end crash countermeasures for heavy trucks.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models which is the focus of this work. This work develops one means to alter the future vehicle states: modulating the driver's throttle commands. First, changes to the longitudinal force are translated to changes in engine torque based on the current operating state (torque and speed) of the engine.

Forward Collision Warning (FCW) and Lane Departure Warning (LDW) systems are two active safety systems that have recently been added to the U.S. New Car Assessment Program (NCAP) evaluation. Vehicles that pass confirmation tests may advertise the presence of FCW and LDW alongside the vehicle's star safety rating derived from crash tests. This paper predicts the number of crashes and injured drivers that could be prevented if all vehicles in the U.S. fleet were equipped with production FCW and/or LDW systems. Models of each system were developed using the test track data collected for 16 FCW and 10 LDW systems by the NCAP confirmation tests. These models were used in existing fleetwide benefits models developed for FCW and LDW. The 16 FCW systems evaluated could have potentially prevented between 9% and 53% of all rear-end collisions and prevented between 19% and 60% of injured (MAIS2+) drivers. Earlier warning times prevented more warnings and injuries.

This paper includes analysis results for the control strategy of the Peugeot 3008 Hybrid4, a diesel-electric hybrid vehicle, under different thermal conditions. The analysis was based on testing results obtained under the different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory (ANL). The objectives were to determine the principal concepts of the control strategy for the vehicle at a supervisory level, and to understand the overall system behavior based on the concepts. Control principles for complex systems are generally designed to maximize the performance, and it is a serious challenge to determine these principles without detailed information about the systems. By analyzing the test results obtained in various driving conditions with the Peugeot 3008 Hybrid4, we tried to figure out the supervisory control strategy.

Production vehicles are commonly characterized and compared using fuel consumption (FC) and electric energy consumption (EC) metrics. Chassis dynamometer testing is a tool used to establish these metrics, and to benchmark the effectiveness of a vehicle's powertrain under numerous testing conditions and environments. Whether the vehicle is undergoing EPA Five-Cycle Fuel Economy (FE), component lifecycle, thermal, or benchmark testing, it is important to identify the vehicle and testing based variations of energy consumption results from these tests to establish the accuracy of the test's results. Traditionally, the uncertainty in vehicle test results is communicated using the variation. With the increasing complexity of vehicle powertrain technology and operation, a fixed energy consumption variation may no longer be a correct assumption.

Response surface methodology (RSM) techniques were applied to develop a predictive model of electric vehicle (EV) energy consumption over the Environmental Protection Agency's (EPA) standardized drive cycles. The model is based on measurements from a synthetic composite drive cycle. The synthetic drive cycle is a minimized statistical composite of the standardized urban (UDDS), highway (HWFET), and US06 cycles. The composite synthetic drive cycle is 20 minutes in length thereby reducing testing time of the three standard EPA cycles by over 55%. Vehicle speed and acceleration were used as model inputs for a third order least squared regression model predicting vehicle battery power output as a function of the drive cycle. The approach reduced three cycles and 46 minutes of drive time to a single test of 20 minutes.

It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.

Conventional viscous shock absorbers, in parallel with suspension springs, passively dissipate the excitation energy from road irregularity into heat waste, to reduce the transferred vibration which causes the discomfort of passengers. Energy-harvesting shock absorbers, which have the potential of conversion of kinetic energy into electric power, have been proposed as semi-active suspension to achieve better balance between the energy consumption and suspension performance. Because of the high energy density of the rotary shock absorber, a rotational energy-harvesting shock absorber with mechanical motion rectifier (MMR) is used in this paper. This paper presents the assessment of vehicle dynamic performance with the proposed energy-harvesting shock absorber in braking process. Moreover, a PI controller is proposed to attenuate the negative effect due to the pitch motion.

Laboratory and on-road vehicle evaluation is conducted on four vehicle models to evaluate and characterize the impacts to fuel economy of real-world auxiliary loads. The four vehicle models in this study include the Volkswagen Jetta TDI, Mazda 3 i-ELOOP, Chevrolet Cruze Diesel, and Honda Civic GX (CNG). Four vehicles of each model are included in this; sixteen vehicles in total. Evaluation was conducted using a chassis dynamometer over standard drive cycles as well as twelve months of on-road driving across a wide range of road and environmental conditions. The information gathered in the study serves as a baseline to quantify future improvements in auxiliary load reduction technology.

Vehicle lightweighting has been a focus of the automotive industry, as car manufacturers seek to comply with corporate average fuel economy (CAFE) and greenhouse gas (GHG) emissions standards for model year (MY) 2017-2025 vehicles. However, when developing a lightweight vehicle design, the automotive industry typically targets maximum vehicle weight reduction at minimal cost increase. In this paper, we consider the environmental impacts of the lightweighting technology options. The materials used for vehicle lightweighting include high-strength steel (HSS), aluminum, magnesium and carbon fiber reinforced plastic (CFRP). Except for HSS, the production of these light materials is more GHG-intensive (on a kg-to-kg basis) compared with the conventional automotive materials they substitute. Lightweighting with these materials, therefore, may partially offset the GHG emission reductions achieved through improved fuel economy.

Dynamic game theory brings together different features that are keys to many situations in control design: optimization behavior, the presence of multiple agents/players, enduring consequences of decisions and robustness with respect to variability in the environment, etc. In the presented methodology, vehicle stability is represented by a cooperative dynamic/difference game such that its two agents (players), namely, the driver and the direct yaw controller (DYC), are working together to provide more stability to the vehicle system. While the driver provides the steering wheel control, the DYC control algorithm is obtained by the Nash game theory to ensure optimal performance as well as robustness to disturbances. The common two-degree of freedom (DOF) vehicle handling performance model is put into discrete form to develop the game equations of motion.

Human expert drivers have the unique ability to combine correlated sensory inputs with repetitive learning to build complex perceptive models of the vehicle dynamics as well as certain key aspects of the tire-ground interface. This ability offers significant advantages for navigating a vehicle through the spatial and temporal uncertainties in a given environment. Conventional traction control algorithms utilize measurements of wheel slip to help insure that the wheels do not enter into an excessive slip condition such as burnout. This approach sacrifices peak performance to ensure that the slip limits are generic enough suck that burnout is avoided on a variety of surfaces: dry pavement, wet pavement, snow, gravel, etc. In this paper, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy.

Limited battery power and poor engine efficiency at cold temperature results in low plug in hybrid vehicle (PHEV) fuel economy and high emissions. Quick rise of battery temperature is not only important to mitigate lithium plating and thus preserve battery life, but also to increase the battery power limits so as to fully achieve fuel economy savings expected from a PHEV. Likewise, it is also important to raise the engine temperature so as to improve engine efficiency (therefore vehicle fuel economy) and to reduce emissions. One method of increasing the temperature of either component is to maximize their usage at cold temperatures thus increasing cumulative heat generating losses. Since both components supply energy to meet road load demand, maximizing the usage of one component would necessarily mean low usage and slow temperature rise of the other component. Thus, a natural trade-off exists between battery and engine warm-up.

Vehicle stability is maintained by proper interactions between the driver and vehicle stability control system. While driver describes the desired target path by commanding steering angle and acceleration/deceleration rates, vehicle stability controller tends to stabilize higher dynamics of the vehicle by correcting longitudinal, lateral, and roll accelerations. In this paper, a finite-horizon optimal solution to vehicle stability control is introduced in the presence of driver's dynamical decision making structure. The proposed concept is inspired by Nash strategy for exactly known systems with more than two players, in which driver, commanding steering wheel angle, and vehicle stability controller, applying compensated yaw moment through differential braking strategy, are defined as the dynamic players of the 2-player differential linear quadratic game.

As vehicle technology progresses to new levels of sophistication, so too, vehicle test methods must evolve. This is true for analytical testing in a laboratory and for on-road vehicle testing. Every year since 1993, the U.S. Department of Energy (DOE) and original equipment manufacturer (OEM) sponsors have organized a series of competitions featuring advanced hybrid electric vehicle (HEV) technology to develop and promote DOE goals in fuel savings and alternative fuel usage. The competition has evolved over many years and has included many alternative fuels feeding the prime mover (including hydrogen fuel cells). EcoCAR turned its focus to plug-in hybrid electric vehicles (PHEVs) and it was quickly realized that to keep using on-road testing methods to evaluate fuel and electricity consumption, a new method needed to be developed that would properly weight depleting operation with the sustaining operation, using the established Utility Factor (UF) method.

This paper outlines the development of a simulation-based process for assessing the handling performance of a given set of tires on a specific vehicle. Based on force and moment data, a Pacejka tire model was developed for each of the five sets of tires used in this study. To begin with, simple handling metrics including under-steer gradient were calculated using cornering stiffness derived from the Pacejka model. This Pacejka tire model was subsequently combined with a 3DOF non-linear vehicle model to create a simulation model in MATLAB/Simulink®. Other handling metrics were calculated based on simulation results to step and sinusoidal (General Motors Company) steering inputs. Calculated performance metrics include yaw velocity overshoot, yaw velocity response time, lateral acceleration response time and steering sensitivity. In addition to this, the phase lag in lateral acceleration and yaw rate of the vehicle to a sinusoidal steering input were also calculated.

While electric vehicles including plug-in hybrid electric vehicles (PHEVs) and battery-powered electric vehicles (BEVs) are considered as promising alternative vehicle/fuel systems to significantly reduce petroleum consumption of the transportation sector, it is important to analyze the emission characteristics and to assess the emission reduction potentials of electric vehicles so that their environmental impacts in terms of climate change, air quality, as well as human health effects could be better understood. To fulfill this objective, we explicitly analyzed the emission characteristics of greenhouse gases (GHG) and criteria air pollutants (CAP, representing VOC, CO, NOx, PM₁₀ and PM₂.₅, and SOx,) of the U.S. power sector, a pivotal upstream sector that impacts the life-cycle GHG and CAP emissions associated with electric vehicles.

The U.S. Department of Energy's Office of Energy Efficiency & Renewable Energy initiated a study that conducted coastdown testing and chassis dynamometer testing of three vehicles, each at multiple test weights, in an effort to determine the impact of a vehicle's mass on road load force and energy consumption. The testing and analysis also investigated the sensitivity of the vehicle's powertrain architecture (i.e., conventional internal combustion powertrain, hybrid electric, or all-electric) on the magnitude of the impact of vehicle mass. The three vehicles used in testing are a 2012 Ford Fusion V6, a 2012 Ford Fusion Hybrid, and a 2011 Nissan Leaf. Testing included coastdown testing on a test track to determine the drag forces and road load at each test weight for each vehicle. Many quality measures were used to ensure only mass variations impact the road load measurements.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Location-Aware Adaptive Vehicle Dynamics (LAAVD) System is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. In contrast to current active safety systems, this system is predictive rather than reactive. This work provides the conceptual groundwork for the proposed system. The LAAVD System employs a predictor-corrector method in which the driver's input commands (throttle, brake, steering) and upcoming driving environment (terrain, traffic, weather) are predicted. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's throttle and brake control are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority.