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To reduce the Body in White (BIW) mass, it is necessary to expand the application of Advanced High-Strength Steels (AHSS) to complex shaped parts. In order to apply AHSS to complex shaped parts with thinner gauge, high formability steel is required. However, higher strength steels tend to display lower elongations, compared with low/medium strength steels. Current AHSS are applied to limited parts for this reason. The new 1.2GPa material, with high formability, was developed to solve this issue. The mechanical property targets for the high elongation 1.2GPa material were achieved by precise metallurgical optimization. Many material aspects were studied, such as formability, weldabilty, impact strength, and delayed fracture. As the result of this development, 1.2GPa AHSS has been applied to a new vehicle launched in 2013.The application of this material was the 1st in the world, and achieved a 11kg mass reduction.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

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.

Rollover crashes have continued to be a source of extensive research into determining both vehicle performance, and occupant restraint capabilities. Prior research has utilized various test procedures, including the FMVSS 208 dolly fixture, as a basis for evaluating vehicle and restraint performance. This research, using 2001 Nissan Pathfinder sport utility vehicles (SUVs), was conducted to update the status of passenger vehicle rollover testing, and evaluate dynamic test repeatability with a new test procedure. A series of eight rollover tests was conducted using these SUV vehicles, mounted on a modified FMVSS 208 rollover dolly fixture, with instrumented dummies in both front seat positions. This test protocol involved launching the vehicles horizontally, after snubbing the dolly fixture, and having the leading-side tires contact curbing for a trip mechanism.

With conventional float-type fuel level sensor's, measurement errors when the vehicle is on an incline or going around corners. Now, a highly-accurate measuring system employing an electrostatic capacity sensor is developed for practical application. This sensor is composed of multiple electrode plates formed alone a allows accurate measurement of regaining fuel even in tanks of irregular shapes. Also, since this sensor comes a unit, it is easier to replace. The system furthermore, employs software provided with averaging, memory storage, and permittivity correction functions in order to elminate the effects of fuel level fluctuations and pressure changes within the tank during driving along with the effect of fuels with different permittivity.

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.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

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.

As a new generation sports car engine to lead the field in the 1990s, a 3.0 liter, 60°V, type 6 cylinder, 4 cam, 24 valve engine (VG30DETT) has been developed to achieve the utmost in high performance levels and reliability. it has been mounted on the new model 300ZX and announced in the North America and Japanese markets. The VG30DETT engine is based on the previous VG30DE engine (the engine mounted on the former model 300ZX designed for the market in Japan). The main components, the major driving and the lubrication systems including such parts as the crank shaft,con-rod, cylinder block, piston, exhaust manifold, and oil pan of the VG30DE were thoroughly reviewed and revised. The VG30DETT engine is the result of redesigning the structure of the engine itself and its parts and components to assure durability under, high-level performance requirements.

Gasoline engines employ various mechanisms for improvement of fuel consumption and reduction of exhaust emissions to deal with environmental problems. Direct fuel injection is one such technology. This paper presents a new quasi-dimensional combustion model applicable to direct injection gasoline engine. The Model consists of author's original in-cylinder turbulence and mixture homogeneity sub model suitable for direct fuel injection conditions. Model validation results exhibit good agreement with experimental and 3D CFD data at steady state and transient operating conditions.

This paper presents a new system for controlling the braking force between the inner and outer wheels in a turn independently. Vehicle cornering performance has improved noticeably in recent years thanks to advances achieved in tire and suspension technology. Due to this improvement, vehicle handling characteristics during braking have taken on added importance. To achieve stabler handling properties during braking in a turn, a new evaluation method is being used at Nissan to analyze vehicle directional stability. The analytical results show that decreasing the yaw moment before wheel locking occurs is effective in achieving stabler handling. An effective approach to decreasing the yaw moment is to control the braking force between the inner and outer wheels independently. Base on these analytical results and experimental data obtained with actual vehicles, a new system has been developed that provides such independent control over the braking force.

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.

Road vehicles can expend a significant amount of energy in undesirable vertical motions that are induced by road bumps, and much of that is dissipated in conventional shock absorbers as they dampen the vertical motions. Presented in this paper are some of the results of a study aimed at determining the effectiveness of efficiently transforming that energy into electrical power by using optimally designed regenerative electromagnetic shock absorbers. In turn, the electrical power can be used to recharge batteries or other efficient energy storage devices (e.g., flywheels) rather than be dissipated. The results of the study are encouraging - they suggest that a significant amount of the vertical motion energy can be recovered and stored.

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 describes a simple engine model at idling and it applies particularly to idle speed control. Through linearization in the neighborhood of the nominal operating points (650 rpm), the engine is expressed as a reduced-order constant coefficient state variable (2 state) model. It was produced through the system order-reduction method. The strategy for controlling idle speed uses the Linear Quadratic and Integral (LQI) optimal control theory. The tracking controller was designed using a state variable engine model, and the performance index was minimized. Since state variables are artificially introduced, they are not directly accessible. Therefore, they must be estimated in accordance with a stored dynamic model (i.e. observer), in which the engine dynamic behavior is estimated on the basis of a state variable model which represents the engine's internal states, in determining controlling values.

This work presents a simple fan model that is based on the actuator disk approximation, and the blade element and vortex theory of a propeller. A set of equations are derived that require as input the rotational speed of the fan, geometric fan data, and the lift and drag coefficients of the blades. These equations are solved iteratively to obtain the body forces generated by the fan in the axial and circumferential directions. These forces are used as momentum sources in a CFD code to simulate the effect of the fan in an underhood thermal management simulation. To validate this fan model, a fan experiment was simulated. The model was incorporated into the CFD code STAR-CD and predictions were generated for axial and circumferential air velocities at different radial positions and at different planes downstream of the fan. The agreement between experimental measurements and predictions is good.

Technologies for improving the fuel economy of gasoline engines have been vigorously developed in recent years for the purpose of reducing CO2 emissions. Increasing the compression ratio is an example of a technology for improving the thermal efficiency of gasoline engines. A significant issue of a high compression ratio engine for improving fuel economy and low-end torque is prevention of knocking under a low engine speed. Knocking is caused by autoignition of the air-fuel mixture in the cylinder and seems to be largely affected by heat transfer from the intake port and combustion chamber walls. In this study, the influence of heat transfer from the walls of each part was analyzed by the following three approaches using computational fluid dynamics (CFD) and experiments conducted with a multi-cooling engine system. First, the temperature rise of the air-fuel mixture by heat transfer from each part was analyzed.

This paper describes a study of drag reduction devices for production pick-up trucks with a body-on-frame structure using full-scale wind tunnel testing and Computational Fluid Dynamics (CFD) simulations. First, the flow structure around a pick-up truck was investigated and studied, focusing in particular on the flow structure between the cabin and tailgate. It was found that the flow structure around the tailgate was closely related to aerodynamic drag. A low drag flow structure was found by flow analysis, and the separation angle at the roof end was identified as being important to achieve the flow structure. While proceeding with the development of a new production model, a technical issue of the flow structure involving sensitivity to the vehicle velocity was identified in connection with optimization of the roof end shape. (1)A tailgate spoiler was examined for solving this issue.