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The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

With ongoing concerns about the elevated levels of ambient air pollution in urban areas and the contribution from heavy-duty diesel vehicles, hybrid electric vehicles are considered as a potential solution as they are perceived to be more fuel efficient and less polluting than their conventional engine counterparts. However, recent studies have shown that real-world emissions may be substantially higher than those measured in the laboratory, mainly due to operating conditions that are not fully accounted for in dynamometer test cycles. At the U.S. EPA National Fuel and Vehicle Emissions Laboratory (NVFEL) the in-use criteria emissions and energy efficiency of heavy-duty class 8 vehicles (up to 36280 kg) can be evaluated under controlled conditions in the heavy-duty chassis dynamometer test.

The article solves the problem of reducing electric power losses of the traction induction machine rotor to prevent its overheating in nominal and high-load modes. Electric losses of the rotor power are optimized by the stabilization of the main magnetic flow of the electric machine at a nominal level with the amplitude-frequency control in a wide range of speeds and increased loads. The quasi-independent excitation of the induction machine allows us to increase the rigidity of mechanical characteristics, decrease the rotor slip at nominal loads and overloads and significantly decrease electrical losses in the rotor as compared to other control methods. The article considers the technology of converting the power of individual phases into a single energy flow using a three-phase electric machine equivalent circuit and obtaining an energy model in the form of equations of instantaneous active and reactive power balance.

The Hybrid Finite Element Analysis (HFEA) method is based on combining conventional Finite Element Analysis (FEA) with analytical solutions and energy methods for mid-frequency computations. The method is appropriate for computing the vibration of structures which are comprised by stiff load bearing components and flexible panels attached to them; and for considering structure-borne loadings with the excitations applied on the load bearing members. In such situations, the difficulty in using conventional FEA at higher frequencies originates from requiring a very large number of elements in order to capture the flexible wavelength of the panel members which are present in a structure. In the HFEA the conventional FEA model is modified by de-activating the bending behavior of the flexible panels in the FEA computations and introducing instead a large number of dynamic impedance elements for representing the omitted bending behavior of the panels.

This work investigates the potential improvements in vehicle fuel economy possible by optimizing gear shift strategies to leverage a novel boosting device, an electrically assisted variable speed supercharger (EAVS), also referred to as a power split supercharger (PSS). Realistic gear shift strategies, resembling those commercially available, have been implemented to control upshift and downshift points based on torque request and engine speed. Using a baseline strategy from a turbocharged application of a MY2015 Ford Escape, a vehicle gas mileage of 34.4 mpg was achieved for the FTP75 drive cycle before considering the best efficiency regions of the supercharged engine.

In order to improve the steering stability of the dual-motor drive electric vehicle, Taking the yaw rate and the sideslip angle as the control variables, Using the improved two degree of freedom linear dynamic model and seven degree of freedom nonlinear vehicle dynamics model, The hierarchical structure is used to establish the dual-motor drive electric vehicle steering stability control strategy which consist of the upper direct yaw moment decision-making layer based on the sliding mode controller and the lower additional yaw moment distribution layer based on the optimization theory. The Matlab/Simulink-Carsim joint simulation platform was built. The control strategy proposed in this paper was simulated and verified under the snake test condition and double-line shift test condition.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

Wet clutches are complex hydrodynamic devices used in both conventional and electrified drivetrain systems. They couple or de-couple powertrain components for applications such as automatic shifting, engine disconnect and torque vectoring. Clutch engagement behaviors vary greatly, depending on design parameters and operating conditions. Because of their direct impact on vehicle drivability and fuel economy, a predictive CAE model is desired for enabling analytical design verification processes. During engagement, a wet clutch transmits torque through viscous shear and asperity contact. A conventional Coulomb’s model, which is routinely utilized in shift simulations, is inadequate to capture non-linear hydrodynamic effects for higher fidelity analysis. Extensive research has been conducted over the years to derive hydrodynamic torque transfer models based on 1D squeeze film or 3D CFD. They are typically coupled with an elastic asperity contact model for mechanical torque transfer.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

Low voltage hybridization (<60 V) supports engine start/stop, regenerative braking, and constrained torque assist/regeneration at a low cost. This work studies the potential benefits of a novel hybrid system, called a power split supercharger (PSS). A 9 kW motor is shared between boosting the engine or providing hybrid functionalities, allowing it to couple with a small engine and still support good acceleration. However, the PSS operation is limited to only one of the parallel hybrid or boosting modes at each time instance. In this work an equivalent consumption minimization strategy (ECMS) is developed to select the PSS mode and the motor torque during hybrid mode. The PSS operation is simulated over standard EPA drive cycles with an engine mean value model that captures detailed air path and PSS dynamics.

This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

Hybridization of vehicles is considered as the most promising technology for automakers and researchers, facing the challenge of optimizing both the fuel economy and emission of the road transport. Extensive studies have been performed on power-split hybrid electric vehicles (PS-HEVs). Despite of the fact that their excellent fuel economy performance in city driving conditions has been witnessed, a bottle neck for further improving the fuel economy of PS-HEVs has been encountered due to the inherent engine-generator-motor power circulation of the power-split system under medium-low speed cruising scenarios. Due to the special mechanical constraints of the power-split device (PSD), the conventional periodic cruising strategy like Pulse and Glide cannot be applied to PS-HEVs directly.

Previous work done at the University of Michigan shows the capability of the vacuum-insulated catalytic converter (VICC) to retain heat during soak and the resulting benefits in reducing cold start emissions. This paper provides an improved version of the design which overcomes some of the shortcomings of the previous model and further improves the applicability and benefits of VICC. Also, newer materials have been evaluated and their effects on heat retention and emissions have studied using the 1-D after treatment model. Cold start emissions constitute around 60% to 80% of all the hydrocarbon and CO emissions in present day vehicles. The time taken to achieve the catalyst light-off temperature in a three-way catalytic converter significantly affects the emissions and fuel efficiency. The current work aims at developing a method to retain heat in catalytic converter, thus avoiding the need for light-off and reducing cold start emissions effectively.

As part of its midterm evaluation of the 2022-2025 light-duty greenhouse gas (GHG) standards, the Environmental Protection Agency (EPA) has been acquiring fuel efficiency data from testing of recent engines and vehicles. The benchmarking data are used as inputs to EPA’s Advanced Light Duty Powertrain and Hybrid Analysis (ALPHA) vehicle simulation model created to estimate GHG emissions from light-duty vehicles. For complete powertrain modeling, ALPHA needs both detailed engine fuel consumption maps and transmission efficiency maps. EPA’s National Vehicle and Fuels Emissions Laboratory has previously relied on contractors to provide full characterization of transmission efficiency maps. To add to its benchmarking resources, EPA developed a streamlined more cost-effective in-house method of transmission testing, capable of gathering a dataset sufficient to broadly characterize transmissions within ALPHA.

Plenoptic particle tracking velocimetry (PTV) shows great potential for three-dimensional, three-component (3D3C) flow measurement with a simple single-camera setup. It is therefore especially promising for applications in systems with limited optical access, such as internal combustion engines. The 3D visualization of a plenoptic imaging system is achieved by inserting a micro-lens array directly anterior to the camera sensor. The depth is calculated from reconstruction of the resulting multi-angle view sub-images. With the present study, we demonstrate the application of a plenoptic system for 3D3C PTV measurement of engine-like air flow in a steady-state engine flow bench. This system consists of a plenoptic camera and a dual-cavity pulsed laser. The accuracy of the plenoptic PTV system was assessed using a dot target moved by a known displacement between two PTV frames.

Wet clutch packs are widely used in today’s automatic transmission systems for gear-ratio shifting. The frictional interfaces between the clutch plates are continuously lubricated with transmission fluid for both thermal and friction management. The open clutch packs shear transmission fluid across the rotating plates, contributing to measurable energy losses. A typical multi-speed transmission includes as many as 5 clutch packs. Of those, two to three clutches are open at any time during a typical drive cycle, presenting an opportunity for fuel economy gain. However, reducing open clutch drag is very challenging, while meeting cooling requirements and shift quality targets. In practice, clutch design adjustment is performed through trial-and-error evaluation of hardware on a test bench. The use of analytical methodologies is limited for optimizing clutch design features due to the complexity of fluid-structure interactions under rotating conditions.