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The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

This paper discusses the development of a Distributed Intelligent Ground Speed Control System, similar to a cruise control system, based on Fuzzy Logic. Fuzzy sets have been developed to input speed error, acceleration and the absolute speed error in order to arrive at a defuzzified output for the impeller clutch control, brake control and the control law selection. A PI controller and a Sliding Mode controller are utilized based on the magnitude of the Absolute Speed Error. A road model is introduced with erratic set speed profiles, which is introduced to replicate a similar situation for a Stop & Go procedure. The system is simulated in a MATLAB/SIMULINK environment and the results indicate smooth and cooperative switching between the controllers stimulated by the Fuzzy Logic Controller.

A methodology has been developed for identifying the combination of battery characteristics which lead to least-cost electric vehicles. Battery interrelationships include specific power vs, specific energy, peak power vs. specific energy and DOD, cycle life vs. DOD, cost vs. specific energy and peak power, and volumetric and battery size effects. The method is illustrated for the “second car” mission assuming lead/acid batteries. Reductions in life-cycle costs associated with future battery research breakthroughs are estimated using a sensitivity technique. A research prioritization system is described.

The Modular Automotive Technology Testbed (MATT) is a flexible platform built to test different technology components in a vehicle environment. This testbed is composed of physical component modules, such as the engine and the transmission, and emulated components, such as the energy storage system and the traction motor. The instrumentation on the tool enables the energy balance for individual components on drive cycles. Using MATT, a single set of hardware can operate as a conventional vehicle, a hybrid vehicle and a plug-in hybrid vehicle, enabling direct comparison of petroleum displacement for the different modes. The engine provides measured fuel economy and emissions. The losses of components which vary with temperature are also measured.

The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

Excessive vibration and poor controllability occur in many mobile fluid power applications, with negative consequences as concerns operators' health and comfort as well as machine safety and productivity. This paper addresses the problem of reducing oscillations in fluid power machines presenting a novel control technique of general applicability. Strong nonlinearities of hydraulic systems and the unpredictable operating conditions of the specific application (e.g. uneven ground, varying loads, etc.) are the main challenges to the development of satisfactory general vibration damping methods. The state of the art methods are typically designed as a function of the specific application, and in many cases they introduce energy dissipation and/or system slowdown. This paper contributes to this research by introducing an energy efficient active damping method based on feedback signals from pressure sensors mounted on the flow control valve block.

Gas engines often utilize a small-volume pre-chamber in which fuel is injected at near stoichiometric condition to produce a hot turbulent jet which then ignites the lean mixture in the main chamber. Hot jet ignition has several advantages over traditional spark ignition, e.g., more reliable ignition of extra-lean mixtures and more surface area for ignition resulting in faster burning and improved combustion burn time. Our previous experimental results show that supersonic jets could extend the lean flammability limit of fuel/air mixtures in the main chamber in comparison to subsonic jets. The present paper investigated the characteristics of supersonic hot jets generated by combustion of stoichiometric H2/air in a pre-chamber to understand the ignition mechanism of ultra-lean mixtures by supersonic hot jets.

There are many challenges in implementing grid aware plug-in electric vehicle (PEV) charging systems with local load control. New opportunities for innovative load control were created as a result of changes to the 2014 National Electric Code (NEC) about automatic load control definitions for EV charging infrastructure. Stakeholders in optimized dispatch of EV charging assets include the end users (EV drivers), site owner/operators, facility managers and utilities. NEC definition changes allow for ‘over subscription’ of more potential EV charging station load than can be continuously supported if the total load at any time is within the supply system safety limit. Local load control can be implemented via compact submeter(s) with locally hosted control algorithms using direct communication to the managed electric vehicle supply equipment (EVSE).

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energy-saving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories.

This paper presents a novel design of a control law optimizing the performance of an on-demand all wheel drive (ODAWD) vehicle with hybrid powertrain for traction enhancement via slip regulation in a driving event. Based on a reasonably simplified vehicle model (bicycle model) and optimization of a performance index based on wheel slip, a closed loop actuator control law is derived. The proposed optimal controller tries to minimize the wheel slip error by activating and dynamically controlling the electric motor drive torque to the non-driven wheel pair (e.g. rear wheels), in order to enhance vehicle longitudinal traction. Simulation of the proposed controller was performed on a validated 14 degree-of-freedom detailed vehicle model in SIMULINK.

Layered materials are one of the most commonly used acoustical treatments in the automotive industry, and have gained increased attention, especially owing to the popularity of electric vehicles. Here, a method to model and couple layered systems with various layer types (i.e., poro-elastic layers, solid-elastic layers, stiff panels, and fluid layers) is derived that makes it possible to stably predict their acoustical properties. In contrast with most existing methods, in which an equation system is constructed for the whole structure, the present method involves only the topmost layer and its boundary conditions at two interfaces at a time, which are further simplified into an equivalent interface. As a result, for a multi-layered system, the proposed method splits a complicated system into several smaller systems and so becomes computationally less expensive.

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

Typically, earthmoving machines do not have wheel suspensions. This lack of components often causes uncomfortable driving, and in some cases reduces machine productivity and safety. Several solutions to this problem have been proposed in the last decades, and particularly successful is the passive solution based on the introduction of accumulators in the hydraulic circuit connecting the machine boom. The extra capacitance effect created by the accumulator causes a magnification of the boom oscillations, in such a way that these oscillations counter-react the machine oscillation caused by the driving on uneven ground. This principle of counter-reacting machine oscillations through the boom motion can be achieved also with electro-hydraulic solutions, properly actuating the flow supply to the boom actuators on the basis of a feedback sensors and a proper control strategy.

Mobile Earth Moving Machinery like Skid-steer loaders have tight turning radius in limited spaces due to a short wheelbase which prevents the use of suspensions in these vehicles. The absence of a suspension system exposes the vehicle to ground vibrations of high magnitude and low frequency. Vibrations reduce operator comfort, productivity and life of components. Along with vibrations, the machine productivity is also hampered by material spillage which is caused by the tilting of the bucket due to the extension of the boom. The first part of the paper focuses on vibration damping. The chassis’ vibrations are reduced by the use of an active suspension element which is the hydraulic boom cylinder which is equivalent to a spring-damper. With this objective, a linear model for the skid steer loader is developed and a state feedback control law is implemented.

This paper determines the impact of ambient temperature on energy consumption of a variety of vehicles in the laboratory. Several conventional vehicles, several hybrid electric vehicles, a plug-in hybrid electric vehicle and a battery electric vehicle were tested for fuel and energy consumption under test cell conditions of 20°F, 72°F and 95°F with 850 W/m₂ of emulated radiant solar energy on the UDDS, HWFET and US06 drive cycles. At 20°F, the energy consumption increase compared to 72°F ranges from 2% to 100%. The largest increases in energy consumption occur during a cold start, when the powertrain losses are highest, but once the powertrains reach their operating temperatures, the energy consumption increases are decreased. At 95°F, the energy consumption increase ranges from 2% to 70%, and these increases are due to the extra energy required to run the air-conditioning system to maintain 72°F cabin temperatures.

A methodology for evaluating life cycle cost of electric vehicles (EVs) to their buyers is presented. The methodology is based on an analysis of conventional vehicle costs, costs of drivetrain and auxiliary components unique to EVs, and battery costs. The conventional vehicle's costs are allocated to such subsystems as body, chassis, and powertrain. In electric vehicles, an electric drive is substituted for the conventional powertrain. The current status of the electric drive components and battery costs is evaluated. Battery costs are estimated by evaluating the material requirements and production costs at different production levels; battery costs are also collected from other sources. Costs of auxiliary components, such as those for heating and cooling the passenger compartment, are also estimated. Here, the methodology is applied to two vehicle types: subcompact car and minivan.

In this paper, a recently-developed algorithmic method of deriving the state equations of power systems containing power electronic components is described. Therein the system is described by the pertinent branch parameters and the circuit topology; however, unlike circuit-based algorithms, the difference equations are not implemented at the branch level. Instead, the composite system state equations are established. A demonstration of the computer implementation of this algorithm to model a variable-speed, constant-frequency aircraft generation system is described. Because of the large number of states and complexity of the system, particular attention is placed on the development of a model structure which provides optimal simulation efficiency.