Search Results

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.

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

Crankshafts of motorcycles require high strength, high reliability and low manufacturing cost. Recently, a reduction of Pb content in the free-cutting steel, which is harmful substance, is required. In order to satisfy such requirements, we started the development of Pb-free free-cutting steel which simultaneously enabled the omission of the normalizing process. For the omission of normalizing process, we adjusted the content of Carbon, Manganese and Nitrogen of the steel. This developed steel can obtain adequate hardness and fine microstructure by air-cooling after forging. Pb-free free-cutting steel was developed based on Calcium-sulfur free-cutting steel. Pb free-cutting steel is excellent in cutting chips frangibility in lathe process. We thought that it was necessary that cutting chips frangibility of developed steel was equal to Pb free-cutting steel. It was found that cutting chips frangibility depend on a non-metallic inclusion's composition, shape and dispersion.

There is a movement to apply emission control in a marine engine as well due to high public awareness of environmental concern in the United States. We started at the development of 3-seater Personal Watercraft (PWC) equipped with 4-stroke engines in taking environment conformity and potential into account. The PWC employed series 4-cylinder 1100cc displacement engine that has been used for mass production motorcycles. The engine was modified to satisfy requirements for PWC, as a marine engine, such as performance function and corrosion. In order to achieve greater or equal power/weight ratio as against two-stroke PWCs, a four-stroke engine for PWC with an exhaust turbo charger was developed. As a result, we succeeded in developing an engine that attained top-level running performance and durability superior to competitors' 2-stroke engines.

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.

In this paper, a comparison between different six-phase machine topologies is conducted considering their technical performance for automotive applications. Asymmetrical and symmetrical configurations, as well as neutral point connection, are considered as candidate topologies and modelled using vector space decomposition (VSD) and double stator or double dq transformations. In both cases, a generalized model to include an arbitrary phase shift between the windings is presented as well as the effect of the neutral connection on the inverter model. For the selection, the steady-state and post-fault performance are considered in terms of control flexibility, fault-tolerant capability, and dc-link voltage utilization. For the latest, the different topologies are evaluated operating in both linear and overmodulation regions based on space vector modulation (SVM).

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 the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

This paper introduces a new type of aluminum radiator that has been developed with the objective of high performance and light weight. Aluminum radiators have recently been replacing copper radiators because of their light weight, but the heat rejection of such conventional alminum radiators does not exceed that of copper radiators. Authors established the aluminum radiator not only being light weight but also having high performance through the following approaches. (1) Optimization of radiator core module. (2) Thickness reduction of tube and fin. (3) Development of aluminum alloys with improved corrosion resistance for tubes and fins. As a result, a new type single-row aluminum radiator has achieved 7% higher rejection at 50% lighter weight than those of copper double-row radiator.

This paper proposes a domain-centralized powertrain E/E (electrical and/or electronic) architecture for all-electric vehicles that features: a powerful master controller (domain controller) that implements most of the functionality of the domain; a set of smart actuators for electric motor(s), HV (High Voltage) battery pack, and thermal management; and a gateway that routes all hardware signals, including digital and analog I/O, and field bus signals between the domain controller and the rest of the vehicle that is outside of the domain. Major functional safety aspects of the architecture are presented and a safety architecture is proposed. The work represents an early E/E architecture proposal. In particular, detailed partitioning of software components over the domain’s Electronic Control Units (ECUs) has not been determined yet; instead, potential partitioning schemes are discussed.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

A variable valving system was developed. This system has two cam profiles, one for low speed and one for high speed. A 1.2-litre DOHC experimental engine using this system was made and mounted in the body of a 2-1itre class passenger car. Test results of this car were compared to those of the same car with its original engine. The test car showed better results in every area of driving performance, in mode-fuel-econorny and in noise tests. This paper presents the mechanism, operation and test results of this variable valving system, the 1.2-litre experimental engine and this passenger car. THE PERFORMANCE AND EFFICIENCY of the passenger car gasoline engine have been greatly improved: primarily as a response to exhaust-gas emission regulations and the oil crises. These improvements have been achieved mainly through the development of control technologies to optimize many parameters such as ignition timing and air fuel ratio precisely according to driving conditions.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

During driving conditions, when it is needed to transition from Electric Vehicle (EV) to Hybrid Vehicle operation, synchronization of the engine with the shaft and transmission is essential to enable clutch engagement and, subsequently, providing engine power to the wheels. Challenges arise when the engine must generate power to move itself and cannot rely on electric motors for precision. Cost-effective hybrid vehicle propulsion architectures which utilize small 12V belt-starter generators (BSGs) to initiate engine activation are inherently affected. In these situations, a speed profile that balance rapid response and control effort while considering system limitations to mitigate undesirable overshoots and delays, is required. This paper presents a Linear Quadratic Integral (LQI) approach to formulate a speed reference profile that ensures optimal engine behavior.

The present work discusses the potential benefits of using computational fluid dynamics (CFD) simulation and artificial intelligence (AI) in the design and optimization of hydrogen internal combustion engines (H2ICEs). A Machine Learning (ML) model is developed and applied to the CFD simulation data to identify optimal injection system parameters on the Sandia H2ICE Engine to improve the mixing. This approach can aid in developing predictive ML models to guide the design of future H2ICEs. For the current engine configuration, it is observed that hydrogen (H2) gas injection contributes mixing of H2 with air. If the injector parameters are optimized, mixture preparation is better and eventually combustion. A base CFD model is validated from the Sandia H2ICE engine data against Particle Image Velocimetry (PIV) data for velocity and Planar Laser Induced Fluorescence (PLIF) data for H2 mass fraction.

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.

Variable-valve-actuation mechanism is considered to be one of the most suitable solutions to realize the compatibility between higher power output and performances in the practical speed range. A new variable-valve-actuation mechanism named “Shuttle Cam” was designed and studied. In this mechanism which was applied to a conventional motorcycle engine with rocker arms and gear-train-driven valve system, the cam gears move along the idler gear. And cam shafts simultaneously slide along the rocker-arm slipper surfaces which are concentric with the idler gear. Consequently valve lift varies continuously in accordance with the alteration in the rocker-arm lever ratio and the cam phasing changes simultaneously in accordance with the cam gear rotation. Result of the experiments has confirmed that the mechanism functions accurately even at high speeds up to 10,000 rpm and some improvements were achieved in power output, fuel consumption, idling quality, and exhaust-noise level.