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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.

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.

Research is under way on an engine system [1] that achieves a variable compression ratio using a multiple-link mechanism between the crankshaft and pistons for the dual purpose of improving fuel economy and power output. At present, there is no database that allows direct judgment of the feasibility of the specific sliding parts in this mechanism. In this paper, the feasibility was examined by making a comparison with the sliding characteristics and material properties of conventional engine parts, for which databases exist, and using evaluation parameters based on mixed elasto-hydrodynamic (EHD) lubrication calculations. In addition, the innovations made to the mixed EHD calculation method used in this study to facilitate calculations under various lubrication conditions are also explained, including the treatment of surface roughness, wear progress and stiffness around the bearings.

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.

Nissan has developed a new GA16DE engine for use in the new 1991 Sentra. The major development aims for this engine were to achieve ample torque at low to intermediate engine speed and smooth throttle response. These aims, of course, had to be compatible with good fuel economy, quietness, maintenance-free operation and high reliability. In addition, It was necessary to achieve a compact package size despite the twin-cam design. All of those objectives have been attained through the use of a super-long and aerodynamic intake system, variable valve timing control, a low friction, maintenance-free, direct acting valve system, dual direction fuel injectors, and a two-stage cam drive system. This paper discuss the major development objectives, basic engine structure and principal component parts.

There has been a growing need to develop more compact automatic transmissions with a greater number of speeds for better fuel economy and better driveability. This study investigated a method for determining suitable planetary gear trains for today's transmissions. A computer program has been developed for application to five-speed transmissions consisting of two planetary gearsets. By analyzing various gear train possibilities, the program can identify which gearsets are suitable for different conditions, including the number of speeds, the number of binding elements, topological suitability and other factors.

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.

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.

A robotic driver has been designed on the basis of an analysis of a human driver's action in following a given driving schedule. The self-learning algorithm enables the robot to learn the vehicle characteristics without human intervention. Based on learned relationships, the robotic driver can determine an appropriate accelerator position and execute other operations through sophisticated calculations using the future scheduled vehicle speed and vehicle characteristics data. Compensation is also provided to minimize vehicle speed error. The robotic driver can reproduce the same types of exhaust emission and fuel economy data obtained with human drivers with good repeatability. It doesn't require long preparation time. Thereby making it possible to reduce experimentation work in the vehicle development process while providing good accuracy and reliability.

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.