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The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 75 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the rigorous design process followed by the Ohio State team during Year 1 of the competition. The design process includes identifying the team customer's needs and wants, selecting an overall vehicle architecture and completing detailed design work on the mechanical, electrical and control systems. This effort was made possible through support from the U.S.

The integrity of the exhaust emission system in a passenger vehicle can best be maintained by monitoring its performance continuously on board the vehicle. It is with the intent of monitoring emission system performance that the California Air Resources Board has proposed regulations which will require vehicles to be equipped with on-board monitoring systems. These proposed regulations are known as OBDII and will probably be followed by similar Federal EPA regulations.This paper discusses a method of monitoring engine misfire as part of the OBDII requirements for passenger vehicle on-board diagnostics. The method is relatively inexpensive in that it uses an existing sensor for measuring instantaneous crankshaft angular position, and utilizes electronic signal processing which can be implemented in relatively inexpensive custom integrated circuits.

An appropriate fault diagnosis and Isolation (FDI) strategy is very useful to prevent system failure. In this paper, a model-based fault diagnosis strategy is developed for an internal combustion engine (ICE) under speed control. Engine throttle fault and the manifold pressure sensor fault are detected and isolated. A nonlinear observer based residual generation approach is proposed. Manifold pressure and throttle are observed. Fault codes are designed with redundancy to prevent bit error. Performance of fault diagnosis strategy has been evaluated with simulations.

Traditionally in the United States, Diesel engines have negative connotations, primarily due to their association with heavy duty trucks, which are wrongly characterized as “dirty.” Diesel engines are more energy efficient and produce less carbon dioxide than gasoline engines, but their particulate and NOx emissions are more difficult to reduce than spark ignition engines. To tackle this problem, a number of after-treatment technologies are available, such as Diesel Lean NOx Traps (LNTs)), which reduces oxides of nitrogen, and the Diesel particulate filter (DPF), which reduces particulate matter. Sophisticated control techniques are at the heart of these technologies, thus making Diesel engines run cleaner. Another potentially unattractive aspect of Diesel engines is noise.

This year, in the third year of FutureTruck competition, the Ohio State University team has taken the challenge to convert a 2002 Ford Explorer into a more fuel efficient and environmentally friendly SUV. This goal was achieved by use of a post-transmission, charge sustaining, parallel hybrid diesel-electric drivetrain. The main power source is a 2.5-liter, 103 kW advanced CIDI engine manufactured by VM Motori. A 55 kW Ecostar AC induction electric motor provides the supplemental power. The powertrain is managed by a state of the art supervisory control system which optimizes powertrain characteristics using advanced energy management and emission control algorithms. A unique driver interface implementing advanced telematics, and an interior designed specifically to reduce weight and be more environmentally friendly add to the utility of the vehicle as well as the consumer appeal.

The Three Way Catalyst (TWC) is an effective pollutant conversion system widely used in current production vehicles to satisfy emissions regulations. A TWC’s conversion efficiency degrades over time due to chemical and/or thermal mechanisms causing the catalyst to age. This reduction in conversion efficiency must be accounted for to ensure full useful life emissions compliance. This paper presents an experimental study of the aging impact on TWC performance. Four TWCs differentiated by their age, given in terms of miles driven, were tested. It is shown that the dynamics of oxygen storage are substantially affected by aging of the TWC. A previously developed physics-based oxygen storage model [1] is subsequently used to incorporate the effect of aging on the total Oxygen Storage Capacity (OSC). Parameter identification results for the different age catalysts show that total oxygen storage capacity decreases substantially with aging and is insensitive to operating conditions.

Plug in hybrid electric vehicles (PHEVs) have gained interest over last decade due to their increased fuel economy and ability to displace some petroleum fuel with electricity from power grid. Given the complexity of this vehicle powertrain, the energy management plays a key role in providing higher fuel economy. The energy management algorithm on PHEVs performs the same task as a hybrid vehicle energy management but it has more freedom in utilizing the battery energy due to the larger battery capacity and ability to be recharged from the power grid. The state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining overall fuel consumption.

Energy management plays a key role in achieving higher fuel economy for plug-in hybrid electric vehicle (PHEV) technology; the state of charge (SOC) profile of the battery during the entire driving trip determines the electric energy usage, thus determining the fuel consumed. The energy management algorithm should be designed to meet all driving scenarios while achieving the best possible fuel economy. The knowledge of the power requirement during a driving trip is necessary to achieve the best fuel economy results; performance of the energy management algorithm is closely related to the amount of information available in the form of road grade, velocity profiles, trip distance, weather characteristics and other exogenous factors. Intelligent transportation systems (ITS) allow vehicles to communicate with one another and the infrastructure to collect data about surrounding, and forecast the expected events, e.g., traffic condition, turns, road grade, and weather forecast.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 51 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the fabrication and control implementation process followed by the Ohio State team during Year 2 of the competition. The fabrication process includes finalizing designs based on identified requirements, building and assembling components, and performing extensive validation testing on the mechanical, electrical and control systems.

Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.

Ensuring the reliable operation of the emissions control system is a critical factor in complying with increasingly stringent exhaust emissions standards. In spite of significant advances, the performance of available diagnostic and test equipment is still amenable to further improvement, especially as it pertains to the diagnosis of incipient and intermittent faults. This paper presents experimental results pertaining to the diagnosis of complete, partial and intermittent faults in various components of the engine emissions control system. The instrumentation used in the study permitted simultaneous and essentially continuous analysis of the exhaust gases and of engine variables. Tests were conducted using a section of the EPA urban driving cycle (I/M 240), simulated by means of a throttle/dynamometer controller.

Legislation pertaining to automobile emissions has caused an increased focus on the cold-start performance of internal combustion engines. Of particular concern is the period of time before all available sensors become active. Present engine control strategies must rely on methods other than feedback control while these sensors are not active. Without feedback control during this critical period, engine emissions performance is not optimized. These conditions cause difficulty in performing comprehensive cold-start experiments. For these reasons, we have developed several methods for feedback control during cold-start to aid in laboratory investigations of engine emissions phenomena.

An approach to fault diagnosis for internal combustion engines is considered. It is based on the estimation of cylinder indicated torque by means of sliding mode observers. Instead of measuring indicated pressure in cylinders directly, crankshaft speed is measured as the input of observers, which estimate the indicated torque. Several engine models are considered with different levels of complexity. The indicated torque estimation using sliding mode observers is based on the equivalent control method. The estimation technique is validated experimently on a research engine.

One of the gray areas in the implementation of regulations limiting the generation of pollutants from mobile sources is the actual effectiveness of the exhaust gas emissions control strategy in vehicles that have been in use for some time. While it is possible today to conduct limited diagnostics with the on-board engine computer by performing periodic checks to verify the validity of the signals measured by the on-board sensors, and to measure tailpipe emissions during routine inspection and maintenance, the task of correlating these measurements with each other to provide an on-line, accurate diagnosis of critical malfunctions has thus far proven to be a very challenging task, especially in the case of misfire.

A number of automotive diagnostic equipment and procedures have evolved over the last two decades, leading to two generations of on-board diagnostic requirements (OBDI and OBDII), increasing the number of components and systems to be monitored by the diagnostic tools. The goal of On-Board Diagnostic is to alert the driver to the presence of a malfunction of the emission control system, and to identify the location of the problem in order to assist mechanics in properly performing repairs. The aim of this paper is to suggest a methodology for the development of an Integrated Powertrain Diagnostic System (EPDS) that can combine the information supplied by conventional tailpipe inspection programs with onboard diagnostics to provide fast and reliable diagnosis of malfunctions.

This paper presents a method for air/fuel ratio control using combustion pressure feedback during cold start to be used as an aid in laboratory experiments. The effects of varying air/fuel ratio during cold start are so profound that small differences in air/fuel ratio can create effects that will mask the effects of significant changes in other variables. The ability to control air/fuel ratio is an important aid in comprehensive emission studies during cold start. This work will facilitate future studies of cold start emissions.

The aim of this paper is to document a three year process of product development of the Formula Lightningtm electric race car constructed at the Ohio State University. Today interest in electric vehicles (EV's) is growing, due to the technological advances in recent years, but also in part due to recent legislation which mandates the introduction of ‘zero emission vehicles’ in California before the end of the century. The definition of ‘zero emission vehicle’ is: a vehicle which does not emit any pollutants during operation. Technologically, the only near term vehicle which meets this definition is an EV. One of the most difficult problems of electric racing is that the usable energy in a given set of batteries is not as easily determined as the amount of fuel in a tank. Also, the motor controllers may limit power output as battery voltage drops, further decreasing the amount of usable energy in a battery set.

The evolution of the diagnostic equipment for automotive application is the direct effect of the implementation of sophisticated and high technology control systems in the new generation of passenger cars. One of the most challenging issues in automotive diagnostics is the ability to assess, to analyze, and to integrate all the information and data supplied by the vehicle's on-board computer. The data available might be in the form of fault codes or sensors and actuators voltages. Moreover, as environmental regulations get more stringent, knowledge of the concentration of different species emitted from the tailpipe during the inspection and maintenance programs can become of great importance for an integrated powertrain diagnostic system. A knowledge-based diagnostic tool is one of the approaches that can be adopted to carry out the challenging task of detecting and diagnosing faults related to the emissions control system in an automobile.

New vehicle technologies open up a vast number of new options for the designer, removing traditional constraints. Though hybrid powertrains have thus far been implemented chiefly to improve the fuel economy of already economical passenger cars, hybrid technology may have even more to offer in a performance vehicle. In the year when the C6 Corvette and two large GM hybrid projects have been unveiled, a new case study looks to combine these ideas and explore the performance limits for the next generation high performance sports car. Through an innovative transmission concept and thoughtful packaging, the next generation Corvette could enhance a 600 HP spark-ignited V-8 (supercharged LS2) with 1200 HP from electric machines, and still meet current emission standards. Such immense tractive power, however, would be useless without an intelligent means of delivering this power to the wheels.

In this paper, the detection and isolation of actuator faults (both measured and commanded) occurring in the engine breathing and the fueling systems of a spark-ignition direct-injection (SIDI) engine are described. The breathing system in an SIDI engine usually consists of a fresh air induction path via an electronically controlled throttle (ECT) and an exhaust gas recirculation (EGR) path via an EGR valve. They are dynamically coupled through the intake manifold to form a gas mixture, which eventually enters the engine cylinders for a subsequent combustion process. Meanwhile, the fueling system is equipped with a high-pressure common-rail injection for a precise control of the fuel quantity directly injected into the engine cylinders. Since the coupled system is highly nonlinear in nature, the fault diagnosis will be performed by generating residuals based on multiple nonlinear observers.