Search Results

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

The performance, life cycle cost, and safety of electric and hybrid electric vehicles (EVs and HEVs) depend strongly on their energy storage system. Advanced batteries such as lithium-ion (Li-ion) polymer batteries are quite viable options for storing energy in EVs and HEVs. In addition, thermal management is essential for achieving the desired performance and life cycle from a particular battery. Therefore, to design a thermal management system, a designer must study the thermal characteristics of batteries. The thermal characteristics that are needed include the surface temperature distribution, heat flux, and the heat generation from batteries under various charge/discharge profiles. Therefore, in the first part of the research, surface temperature distribution from a lithium-ion pouch cell (20Ah capacity) is studied under different discharge rates of 1C, 2C, 3C, and 4C.

In this paper, a consensus framework for cooperative parameter estimation within the vehicular network is presented. It is assumed that each vehicle is equipped with a dedicated short range communication (DSRC) device and connected to other vehicles. The improvement achieved by the consensus for parameter estimation in presence of sensor’s noise is studied, and the effects of network nodes and edges on the consensus performance is discussed. Finally, the simulation results of the introduced cooperative estimation algorithm for estimation of the unknown parameter of road condition is presented. It is shown that due to the faster dynamic of network communication, single agents’ estimation converges to the least square approximation of the unknown parameter properly.

System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

Automotive manufacturers are working towards protecting the global environment by using filters to reduce particulate matter (PM) emissions from their vehicles. There is a growing demand for sensors that detect the small amounts of PM leaking through these filters, as they can aid in performing on-board diagnostics (OBD) and monitoring the function of these filters. Currently, vehicles predominantly use an electric resistance type PM sensor, which applies a voltage between electrodes, collects PM, and senses the generation of PM path. However, in response to tightening regulations on PM-OBD, the response time of the sensor needs to be optimized. Furthermore, the fast response time must not degrade the poisoning resistance in order to ensure durability. To shorten sensor response time, we have developed a 20 μm-gap electrode structure using a cross-section of laminated alumina sheets with printed electrodes, which can form PM paths at small PM amounts.

It is imperative to have accurate tire models when trying to control the trajectory of a vehicle. With the emergence of autonomous vehicles, it is more important than ever before to have models that predict how the vehicle will operate in any situation. Many different types of tire models have been developed and validated, including physics-based models such as brush models, black box models, finite element-based models, and empirical models driven by data such as the Magic Formula model. The latter is widely acknowledged to be one of the most accurate tire models available; however, collecting data for this model is not an easy task. Collecting data is often accomplished through rigorous testing in a dedicated facility. This is a long and expensive procedure which generally destroys many tires before a comprehensive data set is acquired. Using a Vehicle Measurement System (VMS), tires can be modeled through on-road data alone.

There has recently been an increasing need in various automotive monitoring and control systems for a simply structured and highly reliable high-pressure sensor to detect the higher pressures of oils, hydraulic fluids, air and air conditioning refrigerants. A simple, newly devised approach to sealing oil filled high-pressure sensors is introduced in this paper. The new structure utilizes a resin instead of the metal and glass conventionally used for hermetic sealing oil filled high-pressure sensors. This is made possible by the combined use of oils with large effective molecular diameters and carefully optimized design of shape and size of the sealing faces between sensor parts. The use of a sealed metal diaphragm allows for extensive use of the sensor with many different kinds of pressure media and in various applications.

The conversion design strategy, and emissions and performance results for a dedicated propane, vapour injected, 1995 Dodge Dakota truck are reported. Data is obtained from the University of Waterloo entry in the 1997 Propane Vehicle Challenge. A key feature of the design strategy is its focus on testing and emissions while preserving low engine speed power for drivability. Major changes to the Dakota truck included the following: installation of a custom shaped fuel tank, inclusion of a fuel temperature control module, addition of a vaporizer and a fuel delivery metering unit, installation of a custom vapour distribution manifold, addition of an equivalence ratio electronic controller, inclusion of a wide range oxygen sensor, addition of an exhaust gas recirculation cooler and installation of thermal insulation on the exhaust system. A competition provided natural gas catalyst was used.

In this study, a finite element software application (SORPAS®) is used to simulate the effect of pulsing on the expected weld thermal cycle during resistance spot welding (RSW). The predicted local cooling rates are used in combination with experimental observation to study the effect pulsing has on the microstructure and mechanical properties of Zn-coated DP600 AHSS (1.2mm thick) spot welds. Experimental observation of the weld microstructure was obtained by metallographic procedures and mechanical properties were determined by tensile shear testing. Microstructural changes in the weld metal and heat affect zone (HAZ) were characterized with respect to process parameters.

Many ICs are used in various electronic components in automotive applications, such as ECUs (electronic control units) and smart actuators. Automotive ICs required the following features: (1) high integration of analog, digital and output devices; (2) high breakdown voltage for analog devices standing the battery voltage; (3) highly accurate control for analog circuits; (4) susceptibility under harsh operating conditions, such as high ambient temperature and high humidity; and (5) high surge immunity such as ESD (electrostatic discharge) robustness. To meet these requirements, we developed analog and output devices with improved surge endurance based on SOI wafer and trench-dielectric-isolation technologies. Analog circuit applications, especially accurate power management or high-precision solenoid driving, demands stable temperature-compensated output. Load dump and battery-jumping also needs high voltage protection and high noise immunity for these devices.

In Japan, from the viewpoint of ozone layer protection, specified CFCs (Chlorofluorocarbons) phase-out started in 1992 and completed by 1995. HFC-134a (Hydro fluorocarbon-134a) is now dominant in the market. HFC-134a, the replacement, has zero ozone depleting potential, while it still has a higher global warming potential (GWP = 1430). In this paper, efforts of DENSO and the Japanese industry from aspects of refrigerant emission reduction and energy efficiency improvement are introduced.

At present, various efforts are being made in the industrial world to preserve the earth's environment. Automobile industry has to comply with the emission control regulations including NOx and PM and the requirement of reducing CO2 emission from the viewpoint of global warming protection and energy saving. In these situations, diesel engines having a high potential to reduce CO2 emission are attracting much attention. In order to enhance the potential of diesel to reduce CO2 while solving its problems (“slow, dirty, noisy”), common rail systems are vital. DENSO developed an advanced common rail system (CRS) that integrates fuel injectors capable of delivering up to five injection events per combustion cycle at 180MPa injection pressure. This paper describes the injection performance and effects of the 180MPa common rail system and then explains the next generation common rail system.

Customers tend to require more comfortable climate control in vehicles. This paper is concern with a new infrared sensor that detects surface temperature at six separate locations, and a climate control system that incorporates the sensor. In a conventional system using an air temperature sensor and solar radiation sensor, climate conditions are usually controlled according to the thermal load. It is believed that more comfortable climate control can be realized by using an infrared sensor to detect passengers' surface temperature. The sensor consists of a lens, an IC with six thermopiles, a circuit and a case, and has been improved to detect in-cabin surface temperature accurately even under severe environmental conditions. The HVAC system controls the outlet air temperature and mode individually for each seat according to detected temperatures.

In this work, a new air hybrid engine is introduced in which two throttles are used to manage the engine load in three modes of operation i.e. braking, air motor, and conventional mode. The concept includes an air tank to store pressurized air during braking and rather than a fully variable valve timing (VVT) system, two throttles are utilized. Use of throttles can significantly reduce the complexity of air hybrid engines. The valves need three fixed timing schedules for the three modes of operation. To study this concept, for each mode, the results of engine simulations using GT-Power software are used to generate the operating maps. These maps show the maximum braking torque as well as maximum air motor torque in terms of air tank pressure and engine speed. Moreover, the resulting maps indicate the operating conditions under which each mode is more effective. Based on these maps, a power management strategy is developed to achieve improved fuel economy.

The performance characteristics of a commercial lean-NOX trap catalyst were evaluated between 200 and 500°C, using H2, CO, and a mixture of both H2 and CO as reductants before and after different high-temperature aging steps, from 600 to 750°C. Tests included NOX reduction efficiency during cycling, NOX storage capacity (NSC), oxygen storage capacity (OSC), and water-gas-shift (WGS) and NO oxidation reaction extents. The WGS reaction extent at 200 and 300°C was negatively affected by thermal degradation, but at 400 and 500°C no significant change was observed. Changes in the extent of NO oxidation did not show a consistent trend as a function of thermal degradation. The total NSC was tested at 200, 350 and 500°C. Little change was observed at 500°C with thermal degradation but a steady decrease was observed at 350°C as the thermal degradation temperature was increased.

We have developed a novel wafer process for capacitive sensing accelerometer using surface Micro Electrical Mechanical Systems (MEMS) technology and successfully applied to the fabrication process. Our new process combines with a single crystal SOI (Si on Insulator) wafer, high aspect ratio silicon etching and newly developed anhydrous HF/Alcohol etch process of silicon oxides. Although wet conditions such as HF/water etch occurs stiction of mobile structure, our anhydrous HF/Alcohol etch process technology occurs no stiction of mobile structures, because of gas phase (dry) process. In our process, we have achieved smaller-sized sensor chip compared to our conventional 2 axes accelerometer.

Increased interest in global warming issues requires rapid improvements in reduction of CO2 emissions. The automotive industry is placing high importance on improving fuel economy performance across their entire product lines. Charging Management System is a necessary element towards fuel economy improvement. Many of today's charging management systems perform at least two important functions: improving efficiency based on vehicle motion, and detecting battery state of charge. These systems become more complicated as more components (i.e. generators, current sensors and ECU) and software are added. Therefore, it is difficult to develop charging management systems for an entire product line and difficult to retrofit the system for vehicles already in production. A stand-alone charging management system solves these issues. This system is independent of the other vehicle systems. The software for improving fuel economy is installed in the generator or current sensor.

Increasing electric loads in a vehicle causes over-discharge of a battery and drag torque due to an alternator. This paper gives a system concept of vehicle electric power flow management to solve these issues. Its primary function includes preserving electricity in a battery, stabilizing electric bus voltage, interfacing with vehicle torque control system, and improving fuel economy. The key point to realize such a system is a unified structure. It offers ‘Plug and Play’ function for electric power management components. Newly developed Vehicle Electric Power Flow Management System (VEF ) totally controls electric power flow in a vehicle. VEF contains an Electric Power Manager and its functional sub-systems, and controls them with the key parameter ‘electric power’. The sub-system includes Generation, Storage, Conversion, and Distribution to the loads.