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In recent years, improvements in the fuel economy and exhaust emission performance of internal combustion engines have been increasingly required by regulatory agencies. One of the salient concerns regarding general purpose engines is the larger amount of CO emissions with which they are associated, compared with CO emissions from automobile engines. To reduce CO and other exhaust emissions while maintaining high fuel efficiency, the optimization of total engine system, including various design parameters, is essential. In the engine system optimization process, cycle simulation using 0-D and 1-D engine models are highly useful. To define an optimum design, the model used for the cycle simulation must be capable of predicting the effects of various parameters on the engine performance. In this study, a model for predicting the performance of a general purpose SI (Spark Ignited) engine is developed based on the commercially available engine simulation software, GT-POWER.

In CFD (Computational Fluid Dynamics) verification of vehicle aerodynamics, detailed velocity measurements are required. The conventional 2D-PIV (Two Dimensional Particle Image Velocimetry) needs at least twice the number of operations to measure the three components of velocity ( u,v,w ), thus it is difficult to set up precise measurement positions. Furthermore, there are some areas where measurements are rendered impossible due to the relative position of the object and the optical system. That is why the acquisition of detailed velocity data around a vehicle has not yet been attained. In this study, a detailed velocity measurement was conducted using a 3D-PIV measurement system. The measurement target was a quarter scale SAE standard vehicle model. The wind tunnel system which was also designed for a quarter scale car model was utilized. It consisted of a moving belt and a boundary suction system.

A numerical calculation method, which enables the analysis of gear train behavior including non-linear elements in a motorcycle engine, was established. During the modeling process, it was confirmed that factors such as bearing distortion, radial bearing clearance and elastic deformation of a tooth flank could not be neglected because they effect the rotation behavior. To keep a high accuracy, those factors were included in the simulation model, after they were converted into the rigidity elements along the rotational direction of each gear model. In addition, the model was combined with a crankshaft behavior calculation model for a driving and excitation source. A time domain numerical integration method was used to perform the transient response simulation across a wide range of engine speeds. A jump phenomenon of response behavior of the driven gear was predicted that is a characteristic of non-linear response. The phenomenon was also observed in a physical test.

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.

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.

A computer simulation method for motorcycle rider motion in a collision on a passenger car has been developed. The computer simulation results were in two cases of collision, at 45 degree and 90 degree angles against the side of a passenger car. The simulated results were compared to the test results for validation. The simulation software of explicit finite element method (FEM) has been used, because of its capability for expressing accurate shape and deformation. The mesh size was determined with consideration for simulation accuracy and calculation time, and an FEM model of a motorcycle, an airbag, a dummy, a helmet and a passenger car were built. To shorten the calculation time, a part of the model was regarded as a rigid body and eliminated from the contact areas. As a result, highly accurate dummy posture and head velocity at the time of contact on the ground were simulated in the two cases of collision.

Models for off-road vehicles, such as farm equipment and military vehicles, require an off-road tire model in order to properly understand their dynamic behavior on off-road driving surfaces. Extensive literature can be found for on-road tire modeling, but not much can be found for off-road tire modeling. This paper presents an off-road tire model that was developed for use in vehicle handling studies. An on-road, dry asphalt tire model was first developed by performing rolling road force and moment testing. Off-road testing was then performed on dirt and gravel driving surfaces to develop scaling factors that explain how the lateral force behavior of the tire will scale from an on-road to an off-road situation. The tire models were used in vehicle simulation software to simulate vehicle behavior on various driving surfaces. The simulated vehicle response was compared to actual maximum speed before sliding vs. turning radius data for the studied vehicle to assess the tire model.

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 study pertains to motion control algorithms using statistical calculations based on relative displacement measurements, in particular where the rattle space is strictly limited by fixed end-stops and a load leveling system that allows for roll to go undetected by the sensors. One such application is the cab suspension of semi trucks that use widely-spaced springs and dampers and a load leveling system that is placed between the suspensions, near the center line of the cab. In such systems it is possible for the suspension on the two sides of the vehicle to settle at different ride heights due to uneven loading or the crown of the road. This paper will compare the use of two moving average signals (one positive and one negative) to the use of one root mean square (RMS) signal, all calculated based on the relative displacement measurement.

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 research investigates the variation of pedestrian stance in pedestrian-automobile impact using a validated multi-body vehicle and human model. Detailed vehicle models of a small family car and a sport utility vehicle (SUV) are developed and validated for impact with a 50th percentile human male anthropometric ellipsoid model, and different pedestrian stances (struck limb forward, feet together, and struck limb backward) are investigated. The models calculate the physical trajectory of the multi-body models including head and torso accelerations, as well as pelvic force loads. This study shows that lower limb orientation during a pedestrian-automobile impact plays a dominant role in upper body kinematics of the pedestrian. Specifically, stance has a substantial effect on the subsequent impacts of the head and thorax with the vehicle. The variation in stance can change the severity of an injury incurred during an impact by changing the impact region.

In order to minimize occupant injury in a vehicle collision, an approach was attempted to address this issue by optimizing the waveform of the vehicle body deceleration to reduce the maximum deceleration applied to the occupant. A previous study has shown that the mathematical solution to the optimal vehicle deceleration waveform comprised three stages: high deceleration, negative deceleration, and constant deceleration. A kinematic model with separated mass of the vehicle was devised to generate the optimal vehicle deceleration waveform comprising three stages including a one with negative deceleration in the middle. The validity of this model has been confirmed by a mathematical study on a one-dimensional lumped mass model. The optimal vehicle deceleration waveform generated by this method was then validated by a three-dimensional dummy simulation.

In order to minimize occupant injury in a vehicle crash, an approach was attempted to address this issue by making the wave form of vehicle body deceleration optimal to lower the maximum value of the occupant deceleration. Prior study shows that the mathematical solutions for the optimal vehicle deceleration wave form feature consisting of three aspects: high deceleration, negative deceleration, and constant deceleration. A kinematical model which has separated mass of the vehicle was devised to generate an optimal vehicle deceleration wave form which consists of three segments including a segment of negative deceleration in the middle. The validity of this model has been certified by a mathematical study by using a one-dimensional lumped mass model. The effectiveness of the optimal vehicle deceleration wave form generated by this method was validated by a simulation with a three-dimensional dummy.

Modeling the tire forces and moments (F&M) generation, during combined slip maneuvers, which involves cornering and braking/driving at the same time, is essential for the predictive vehicle performance analysis. In this study, a new semi-empirical method is introduced to estimate the tire combined slip F&M characteristics based on flat belt testing machine measurement data. This model is intended to be used in the virtual tire design optimization process. Therefore, it should include high accuracy, ease of parameterization, and fast computational time. Regression is used to convert measured F&M into pure slip multi-dimensional interpolant functions modified by weighting functions. Accurate combined slip F&M predictions are created by modifying pure slip F&M with empirically determined shape functions. Transient effects are reproduced using standard relaxation length equations. The model calculates F&M at the center of the contact patch.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.

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.