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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.

An inexpensive driving simulation system with sufficient fidelity has been developed. The system produces motion cues of four degrees of freedom, visual and auditory cues, and control feel on the steering wheel. This paper describes the features of this newly developed system and gives examples that demonstrate its effectiveness. The motion cues provided in this system are yaw, heave, and lateral and fore/aft accelerations. The lateral and fore/aft accelerations are simulated by tilting the simulator compartment. A computer-processed road image is given through a CRT monitor. The restoring torque of the steering wheel is produced by an electrical servosystem via a coil spring. Cruising sound is given in order to improve speed perception. Since the system uses digital computers, the vehicle characteristics are altered easily by merely rewriting the software. This enables us to simulate special vehicle dynamics such as front & rear wheel steering.

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).

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

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.

This paper reports the development of the road traffic simulator to analyze the effectiveness of the rapid charge station (ST). We employ Digital Map 2500 (Spatial Data Framework) as the basic database for the map model in the traffic simulator. The EV behavior is defined based on the traffic database of Road Traffic Census. The road map model in this traffic simulation corresponds to the road map of 10 cities, where the area and population are 2600 km₂ and about three million people, respectively. We found the 17 STs are enough to keep the averaged travel length for a household vehicle in Japan. This result suggests the area responsible for a ST is about 150 km₂ per ST (i.e. one ST in the area of about 12 km square).

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.

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.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

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.

Driving tests were conducted using an experimental vehicle equipped with an adaptive cruise control system incorporating low-speed following capability in order to evaluate drivers' trust in a driver support system. The results revealed that the drivers' trust in the system declined in cases where the control algorithm produced vehicle behavior that was inconsistent with their expectations. However, that decline in trust ceased to be observed as the drivers' understanding of the system improved. This result suggests a correlation between their understanding of the system and trust in it.

Various radar systems have been proposed as collision avoidance sensors for automatic braking and warning applications. Practical use of laser radar systems is near with the introduction of high power, high reliability laser diodes. Utilizing these new devices, a laser radar system has been adapted for measuring the distance to objects in its path. It was first shown that reflectors on the rear of the automobile possess high reflectivity and sharp directivity. Given these characteristics, a compact laser radar system was tested that employed 12W laser diodes and PIN photodiodes. The maximum range of approximately 100 m was obtained. Furthermore, the ability to discriminate other vehicles from roadside objects was achieved by detecting discontinuity in measured distance data through a microprocessor. These results show that the performance of laser radar is comparable to that of microwave radar.

A study was made on a control method for an adaptive cruise control (ACC) system that uses vehicle-to-vehicle communication to achieve a substantial improvement in string stability and natural headway distance response characteristics at lower levels of longitudinal G. A control system using model predictive control was constructed to achieve this desired ACC vehicle behavior. Control simulations were performed using experimental data obtained in vehicle-following driving tests conducted on a proving ground course using a platoon of three manually driven vehicles. The results showed that the proposed ACC system satisfactorily achieved higher levels of required ACC performance.