Search Results

A pioneering approach to implement transfer path analysis (TPA) is proposed in this paper through applying it to an automobile. We propose to use particle velocity as a measure of TPA, in addition to using sound pressure as a conventional measure for TPA. These two quantities together will give a comprehensive and complete definition of sound. Although sound pressure is a scalar, while particle velocity is a vector, it is also proposed that the same technique of the conventional sound pressure TPA should be independently applicable to each component of particle velocity vector. This has been experimentally verified with a study on our test box system. In this paper, we apply the proposed TPA to an actual vehicle to examine its applicability, advantages and limitations. The driving motor sound of a hybrid electric vehicle is chosen as the case study. A tri-axial particle velocity sensor which also measures sound pressure at the same point is utilized in the experiment.

The field of automotive engineering has devoted much research to reduce fuel consumption to attain sustainable energy usage. Friction reductions in powertrain components can improve engine fuel economy. Quantitative accounting of friction is complex because it is affected by many physical aspects such as oil viscosity, temperature, surface roughness and component rotation speed. The purpose of this paper is two-fold: first, to develop a useful tool for evaluating the friction in engine and accessories based on test data; second, to exercise the tool to evaluate the fuel economy gain in a drive cycle for several friction reduction technologies.

To prevent the severe consequences of unsafe driving behaviors, it is crucial to monitor and analyze the state of the driver. Developing an effective driver state monitoring (DSM) systems is particularly challenging due to limited computation capabilities of embedded systems in automobiles and the need for finishing processing in real-time. However, most of the existing research work was conducted in a lab environment with expensive equipment while lacking in-car benchmarking and validation. In this paper, a DSM system that estimates driver's alertness and drowsiness level as well as performs emotion detection built with a cost-effective embedded system is presented. The proposed system consists of a mono camera that captures driver's facial image in real-time and a machine learning based detection algorithm that detects facial landmark points and use that information to infer driver's state.

Demand for predictive powertrain control is rapidly increasing with the recent advancement of Advanced Driving Assistance Systems (ADAS) and Autonomous Driving (AD). The full or semi-autonomous functions could be leveraged to realize better user acceptance as well as powertrain efficiency of the connected vehicle utilizing the proposed Drive Horizon. The sensors of automated driving provide perception of surrounding driving environment which is required to safely navigate the vehicle in real-world driving scenarios. The proposed Drive Horizon provides real-time forecast of driving environment that a vehicle will encounter during its entire travel. This paper summarizes the vehicle’s future speed prediction technique which is an integral part of Drive Horizon for optimized energy control of the vehicle. The prediction model has been developed that integrates information from multiple sources including vehicle GPS, traffic information and map data.

Engine performance has been investigated of currently gasoline powered passenger car engines converted to methanol and gasoline mixtures. A 4 cycle, 4 cylinder, 1.6 liter displacement engine for a conventional passenger car was tested varying the fueling condition. The mixing ratio of methanol to gasoline was changed from zero percent to one hundred percent, discreetly. Evaluation of engine performance was made to find the optimum air-fuel ratio and spark timing in each mixed fuel condition. It has been clarified that the stoichiometric air-fuel ratio in the mixed fuel can be determined by the mixing ratio P, as an expression of The MBT(minimum spark advance for the best torque) characteristics for each mixed fuel codition show that the large retardation of spark timing will be required for the higher mixture ratio fuels. Changes in characteristics of fuel supply and air-fuel ratio sensing devices were investigated experimentally.

This paper proposes a new development method for highly reliable real-time embedded control systems using a CPU model-based hardware/software co-simulation. We take an approach that allows the full simulation of the virtual mechanical control system including CPU and object code level software. In this paper, Renesas SH-2A microcontroller model was developed on CoMET™ platform from VaST Systems Technology. A ETC (Electronic Throttle Control) system and engine control system were chosen to prove this concept. The ETB (Electronic Throttle Body) model on Saber® simulator from Synopsys® or engine model on MATLAB®/Simulink® simulator from MathWorks can be simulated with the SH-2A model. To help the system design, debug and evaluation, we developed an integrated behavior analyzer, which can display CPU behavior graphically during the simulation without affecting the simulation result, such as task level CPU load, interrupt statistics, software variable transition chart, and so on.

In this research, the feasibility of FSW dissimilar magnesium alloys was investigated. Specifically, die cast MRI-153M and wrought AZ31 were butt-welded. The effects of weld parameters such as tool rotation and weld speed on the weld quality have been characterized in terms of macrostructure, microstructure, hardness profile as well as tensile strength. A relatively wide FSW process window exists to achieve void-free welds. In the stir zone MRI-153M transforms from cast structure to fine grain structure during FSW, while AZ31 transforms from originally twinned grains to equi-axed grains without twins. MRI-153M in the stir zone becomes stronger than the base metal and the stir zone on the AZ31 side becomes weaker than the base metal. Tensile coupons failed in the base metal of MRI-153M, and the tensile strength was found to depend on the quality of the base metal of MRI-153M.

Friction stir welding (FSW) shows advantages for joining lightweight alloys for automotive applications. In this research, the feasibility of friction stir welding aluminum for an automotive component application was studied. The objective of this research was to improve the Friction Stir Spot Welding (FSSW) technique used to weld an aluminum closure panel (CP). The spot welds were made using the newly designed swing-FSSW technique. In a previous study (unpublished), the panel was welded from the thin to thick side using both an 8 mm and a 10 mm diameter tool. The 10 mm tool passed various fatigue tests; however, the target was to improve performance of the 8 mm tool, especially to increase the number of cycle before the first crack appearance during fatigue testing. In this study fatigue tests and static strength was recorded for weld specimens that were welded from thick-to-thin with an 8 mm diameter tool.

A new hydrogen flow sensor was designed and evaluated based on the concept of hot wire anemometry. This sensor is designed to measure the mass flow rate of hydrogen gas used in (but not limited to) proton exchange fuel cell, PEFC. The conceptual evaluation was initiated by deriving an electro-thermal model of the hot wire required for sensing hydrogen velocity. The modeling is done via a mechatronics software tool, Saber™. This model was validated using air as a medium. Simulated and experimental performance results and safety issues are presented and discussed in this paper. Fail safe methods and effectiveness have been investigated along with hydrogen ignition temperatures with varying hydrogen to air ratio.

To detect an air-fuel ratio in wide range is very important to control the automotive engines with low fuel consumption and low exhaust emissions. Although the application of zirconia electrolyte for this purpose has been proposed by the authors several years ago, there remained several problems due to the contamination of gas diffusion apertures which are exposed to the exhaust gas environment. Here the behavior of ions transported in zirconia electrolyte have been analyzed to optimize the structure and characteristics, and to guarantee the long life operation of sensor. Gas contents and their reactions in combustion process under the wide range air-fuel ratio have been analyzed, and these results were reflected to the analysis of ion transportation in zirconia electrolyte. Experimental results supported the analytical results, and they showed the possibilities of long life operation of zirconia air-fuel ratio sensor utilizing ion transportation phenomena.

Predicting the vibration of a motor gearbox assembly driven by a PWM inverter in the early stages of development is demanding because the assembly is one of the dominant noise sources of electric vehicles (EVs). In this paper, we propose a simulation model that can predict the transient vibration excited by gear meshing, reaction force from the mount, and electromagnetic forces including the carrier frequency component of the inverter up to 10 kHz. By utilizing the techniques of structural model reduction and state space modeling, the proposed model can predict the vibration of assembly in the operating condition with a system level EV simulator. A verification test was conducted to compare the simulation results with the running test results of the EV.

This study deals with general consideration points of developing a rear bumper integrated carrier system, especially in view of optimized layout for SUV.

There is accumulating evidence that drowsy driving is one of the leading causes of vehicle crashes and accidents worldwide. Consequently, automotive manufacturers started to develop in-vehicle drowsiness detection devices. However, due to the limited computation resources and the complexity of the vehicular environment, the existing products' performance is limited. Moreover, the vast majority of the commercialized products focus on monitoring the subject's current drowsiness level, whereas predicting drowsiness level in advance to avoid future risks is overlooked. In this research, a multi-time-scale fusion approach is proposed where prediction results from both long-term and short-term Recurrent Neural Networks (RNN) were combined to predict a person's drowsiness level. Our results indicate that the proposed fusion strategies can successfully capture both the short-term microsleep-related features and long-term sleepiness features and improve the drowsiness prediction performance.

Emissions tests were performed to study the operating characteristics of a wide range air/fuel ratio (AFR) sensor in closed loop control. The AFR sensor used here has an output voltage with respect to AFR that is linear and can be characterized by a fourth order polynomial function. For this study the output signal of the AFR sensor was fed into a General Control Unit (GCU). The GCU converted this analog input signal into a square wave similar to a lambda sensor. The output from the GCU was fed into the Engine Control Unit (ECU) of the 3.8L, V6 test engine to control the engine A/F ratio. Emissions tests were conducted in closed loop mode under steady state and transient condition. Emissions of HC, CO and NOx using the AFR sensor will be shown. Results of these tests showed that the AFR sensor allowed for precise control of the AFR at the stoichiometric point (λ = 1.0).

The usage of Compressed Natural Gas (CNG) engines is increasing as requirements for cleaner emissions are required by state and federal agencies such as C.A.R.B. and E.P.A. Also, to further reduce emission levels, tighter air/fuel ratio control is required. There are many ways to control air/fuel ratio on a CNG engine. It can be performed in a feedforward method, a feedback method or a combination of both. CNG fuel can be introduced to the engine via single-point injection, multi-point injection or with an air/gas mixer. Mixer-type and single-point injection are good candidates for the application of a gas flow sensor for accurate air/fuel ratio measurement. Reduction of valve hysteresis can also be achieved. Fuel delivery and control systems cost can be kept low compared to using multi-point injection where high flow injectors are required for each cylinder. A gas flow sensor is placed in the CNG stream to monitor mass gas flow rate.

A multiplexed transmission system utilizing newly developed optical polymer has been proposed. The system is composed of a star-shaped optical network, in which optical signals can be transmitted bi-directionally through a fiber and optical branches between the central and local controllers. The new polymer optical fiber has been developed and adopted for this system, and it was designed to be durable to the high temperature in automotive engine rooms. The high temperature resistibility of the fiber has been achieved with utilization of a thermo-setting resin for the core materials. The optical loss characteristics of the fiber is as low as 0.50 dB/m at 660 nm wavelength.

Stereo vision based sensing systems have gained significant attention during the last two decades due to its reliable and accurate obstacle detection and recognition capabilities. Such systems with advanced processing units are now widely used in partially automated vehicles to improve passengers’ safety and comfort level. A predictive suspension control system that could provide better ride comfort and safety to the passengers by detecting potholes in advance and control the suspension system accordingly has been investigated in this study. Potholes can become serious safety hazard and can often cause discomfort if not detected and maneuvered at the right time. In this paper, a novel stereo vision based pothole detection system is proposed that detects pothole and calculates its depth accurately.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

Automotive vehicles today consist of very complex network of electronic control units (ECU) connected with each other using different network implementations such as Controller Area Network (CAN), FlexRay, etc. There are several ECUs inside a vehicle targeting specific applications such as engine, transmission, body, steering, brakes, infotainment/navigation, etc. comprising on an average more than 50 ECUs executing more than 50 million lines of software code. It is expected to increase exponentially in the next few years. Such complex electric/electronic (E/E) architecture and software calls for a comprehensive, flexible and systematic development and validation environment especially for a system level or vehicle level development. To achieve this goal, we have built a virtual multi-ECU high fidelity cyber-physical multi-rate cosimulation that closely resembles a realistic hardware based automotive embedded system.

As the automotive industry undergoes significant changes in the dynamic behavior of vehicles and increasing demand for rapid product design, accurate prediction of product performance in the early stages has become more crucial than ever in the competitive environment. Shim-stack-type hydraulic dampers are widely used in automotive parts for both internal combustion engine (ICE) vehicles and electric vehicles (EV). EVs are even more sensitive to damper performance as ICE, which is a major NVH source has been removed. However, the industry still faces challenges in obtaining accurate models of dampers due to their highly nonlinear hydro-mechanical behavior. Bleed slits in a shim-stack-type hydraulic damper play a key role in determining the blow-off characteristics of dampers, and therefore, accurate prediction of the blow-off characteristics is crucial in evaluating the damping performance of a vehicle.