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A numerical modeling technique is proposed for computer simulations of high speed valve train dynamics. The dynamic terms in the valve spring reaction forces are calculated using linear vibration theory for given kinematic valve motions. Because the spring dynamics are analyzed before the time stepping integration, spring surge phenomena can be included without using additional computer time. Consequently, valve train dynamics can be simulated very quickly without noticeable errors in accuracy. The experimental results prove the computer model developed here is accurate and also computationally efficient.

Motorcycle usage continues to expand globally. Motorcycles use various fuels in different countries and regions, and it is required that they comply with emissions and fuel consumption regulations as specified in UN-GTR No.2 (WMTC). In general, a motorcycle engine has a large bore diameter and a high compression ratio due to demands of high performance. Poor fuel quality may cause damage to the engine, mainly by knocking. Knock control systems utilizing high-frequency vibration detection strategies like knock sensors, which are equipped on several sport-touring motorcycles, are not used widely for reasons of complex construction and high cost. This research aims to develop a new concept of combustion control for common motorcycle as an alternative.

The Drive By Wire (hereafter referred to as DBW) system is the electronically throttle control system. It controls a throttle valve in order to aim at a suitable throttle position according to an engine operating condition and a demand of driver or rider. This system is basically composed of a throttle body with driving motor, an Accelerator Position Sensor (hereafter referred to as APS), and an Electronic Control Unit (hereafter referred to as ECU). The DBW system is spreading to motorcycle field as replacement of existing mechanical intake control system. This is because there are some advantages as the following especially in the large displacement model: capability for installation of several functions, flexibility in adaptation to recent environmental regulations, and effect on reduction of system cost, etc. In general, the motorcycle has some unique features compared with the automobile. Among them, important features for the DBW system are following three points.

The performance of Gasoline Direct Injection (GDI) engines is governed by multiple physical processes such as the internal nozzle flow and the mixing of the liquid stream with the gaseous ambient environment. A detailed knowledge of these processes even for complex injectors is very important for improving the design and performance of combustion engines all the way to pollutant formation and emissions. However, many processes are still not completely understood, which is partly caused by their restricted experimental accessibility. Thus, high-fidelity simulations can be helpful to obtain further understanding of GDI injectors. In this work, advanced simulation and experimental methods are combined in order to study the spray characteristics of two different 3-hole GDI injectors.

This paper presents a cohesive set of engine-in-the-loop (EIL) studies examining the use of hierarchical model-predictive control for fuel consumption minimization in a class-8 heavy-duty truck intended to be equipped with Level-1 connectivity/automation. This work is motivated by the potential of connected/automated vehicle technologies to reduce fuel consumption in both urban/suburban and highway scenarios. The authors begin by presenting a hierarchical model-predictive control scheme that optimizes multiple chassis and powertrain functionalities for fuel consumption. These functionalities include: vehicle routing, arrival/departure at signalized intersections, speed trajectory optimization, platooning, predictive optimal gear shifting, and engine demand torque shaping. The primary optimization goal is to minimize fuel consumption, but the hierarchical controller explicitly accounts for other key objectives/constraints, including operator comfort and safe inter-vehicle spacing.

Spray characteristics of injectors depend on, among other factors, not only the level of turbulence upstream of the nozzle plate, but also on whether cavitation arises. "Bulk" cavitation, by which we mean cavitation which arises far from walls and thus far from streamline curvature associated with salient points on a wall, has not been investigated thoroughly experimentally and moreover it is quite challenging to predict by means of computational fluid dynamics. Information about the effect of the injector geometry on the formation of bulk cavitation and quantitative measurements of the flow field that promotes this phenomenon in gasoline injectors does not exist and this forms the background to this work. Evolution of bulk cavitation was visualized in two gasoline multi-hole injectors by means of a fast camera.

Fuel spray injected by a port injector has significant effects on engine power output and combustion efficiency. For this reason, it is necessary to atomize fuel into fine droplets and accurately supply it without being susceptible to any changes in temperature or negative pressure affected by engine. This document introduces an atomization technique with optimized layout of nozzle holes and drastically reduced pressure loss (energy loss) in the flow under a needle valve seat. It also describes an injector having a short fuel flow path and a small dead volume under the valve seat, which can have good resistance against any changes in temperature and negative pressure.

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping.

As vehicles are getting electrified and more intelligent, the energy consumption of the auxiliary system increases rapidly. The auxiliary battery acts as the backbone of the system to support the proper operation of the vehicle. It is important to ensure the auxiliary battery has enough energy to meet the basic loads regardless the vehicle is in park or running. However, the existing methods only focus on auxiliary energy management when the vehicle is in a dynamic event. To fulfill the gap, we propose an intelligent strategy that detects the low state of charge (SOC) condition, temporarily turns down the auxiliary loads based on their priorities and charges the auxiliary battery at the maximum efficiency of the auxiliary power unit. In addition, the proposed strategy allows the vehicle to get the park duration update and make intelligent decisions on charging the auxiliary battery.

This paper introduces an intelligent energy distribution scheme for series plug-in hybrid electric vehicles (PHEVs) which incorporates the complexity of human driving behavior. Driving styles can have a significant impact on fuel consumption, but it is often unclear how one should drive to get the optimal fuel efficiency. Hybrid electric vehicles have been shown to improve fuel economy, reduce vehicle emissions and maintain drivability by incorporating electric motors into the drivetrain. Due to the highly complex system design and vehicle architecture, sophisticated energy management strategies (EMS) are required to optimize the vehicle performance. Currently, the power management system is based on static thresholds optimized on a fixed drive cycle for a given vehicle. This paper introduces an adaptive control method for EMS utilizing the complexity of human driving patterns for energy distribution in a series PHEV.

Reynolds-averaged Navier-Stokes (RANS) turbulence model has been used extensively for diesel engine simulations due to its computational efficiency and is expected to remain the workhorse computational fluid dynamics (CFD) tool for industry in the near future. Alternatively, large eddy simulations (LES) can potentially deal with complex flows and cover a large disparity of turbulence length scales, which makes this technique more and more attractive in the engine community. An n-dodecane spray flame (Spray A from Engine Combustion Network) was simulated using a dynamic structure LES model to understand the transient behavior of this turbulent flame. The liquid spray was treated with a traditional Lagrangian method and the gas-phase reaction was closed using a delta probability density function (PDF) combustion model. A 103-species skeletal mechanism was used for n-dodecane chemical kinetic model.

An optically accessible single-cylinder high-speed direct-injection (HSDI) Diesel engine equipped with a Bosch common rail injection system was used to study the spray and combustion processes for European low sulfur diesel, bio-diesel, and their blends at different blending ratio. Influences of injection timing and fuel type on liquid fuel evolution and combustion characteristics were investigated under similar loads. The in-cylinder pressure was measured and the heat release rate was calculated. High-speed Mie-scattering technique was employed to visualize the liquid distribution and evolution. High-speed combustion video was also captured for all the studied cases using the same frame rate. NOx emissions were measured in the exhaust pipe. The experimental results indicated that for all of the conditions the heat release rate was dominated by a premixed combustion pattern and the heat release rate peak became smaller with injection timing retardation for all test fuels.