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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.

The impact of cold-start emissions is well known on conventional and hybrid electric vehicles. Plug-in electric vehicles offer a unique challenge in that there are opportunities for prolonged engine-off conditions which can lead to catalyst cooling and elevated emissions on engine re-start. This research investigates the development and validation of a system for controlling emissions under these conditions, with an emphasis on a catalytic converter model used for design and analysis. The model is a one-dimensional, lumped-parameter model of a three-way catalytic converter developed in Matlab/Simulink. The catalyst is divided into discrete, axial elements and each discrete element contains states for the temperatures of the gas, substrate, and can wall. Heat transfer mechanisms are modeled from physics-based equations.

Over the years numerous researchers have suggested that the ionization current signal carries within it combustion relevant information. The possibility of using this signal for diagnostics and control provides motivation for continued research in this area. To be able to use the ion current signal for feedback control a reliable estimate of some combustion related parameter is necessary and therein lies the difficulty. Given the nature of the ion current signal this is not a trivial task. Fei An et al. [1] employed PCA for feature extraction and then used these feature vectors to design a neural network based classifier for the estimation of air to fuel ratio (AFR). Although the classifier predicted AFR with sufficient reliability, a major draw back was that the ion current signals used for prediction were averaged signals thus precluding a cycle to cycle estimate of AFR.

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.

This paper shall present the design and development of a family of high power, high-speed transport and combat vehicles based on a common module. The system looks to maximize performance at both high-speed operation and low-speed, heavy/severe-duty operation. All-wheel drive/steer-by-wire autonomous traction modules provide the basis for the vehicle family. Each module can continuously develop 300-400 kW of power at the wheels and has nearly double peak capability, exploiting the flexibility of the electric traction system. The maximum starting tractive effort developed by one module can reach 10-15 tons, and the full rated power can be produced at speeds of 100 mph. This paper will present the design and layout of the autonomous modules. Details will be provided about the tandem electric axles, with electric differentials and independent steering.

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle.

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.

The process of incorporating the spark plug as a combustion probe, to perform misfire and knock detection, air to fuel ratio and spark timing control has been the subject of research for some time now. [3], [4]. The feasibility of the approach however depends on being able to correlate some characteristic of the ion current signal to the in cylinder combustion process. Shimaski et al. [3] and Miyata et al. [4] suggest such a relationship. The objective of this research has been to extract combustion information from the measured ion current flowing between spark plug electrodes by using various advanced signal processing methods, and to develop a methodology that will permit combustion diagnostics and possibly control based on these measurements. Tests were carried out on a single-cylinder, methane-fueled CFR engine.

Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.

Strong dependency on crude oil in most areas of modern transportation needs lead into a significant consumption of petroleum resources over many decades. In order to maximize the effective use of remaining resources, various types of powertrain topologies, such as hybrid configurations among fuel cell, electric battery as well as conventional IC engine, have been proposed and tested out for number of vehicle classes including a personal commuting vehicle. In this paper the vehicle parameters are based on a typical commercial sub-compact vehicle (FIAT Panda) and energy needs are estimated on the sized powertrain. The main control approach is divided in two categories: off-line global optimization with dynamic programming (DP, not implementable in real time), and on-line Proportional and Feed-Forward with PI controllers. The proposed control approaches are developed both for charge-sustaining and charge-depleting mode and sample results are shown and compared.

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 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 design of a performance hybrid electric vehicle includes a wide range of architecture possibilities. A large part of the design process is identifying reasonable vehicle architectures and vehicle performance capabilities. The Ohio State University EcoCAR 3 team designed a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of reducing the environmental impact of the vehicle, the Ohio State Camaro has been designed with a 44-mile all-electric range. It also features an 18.9 kWh Li-ion energy storage system, a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85) fuel, a 5-speed automated manual transmission, and a 150 kW peak electric machine. This report details the design and modeling process followed by the Ohio State team during Year 1 of the competition. The process included researching the customer needs of the vehicle, determining team design goals, initial modeling, and selecting a vehicle architecture.

EcoCAR 2: Plugging in to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 30 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The EcoCAR 2 VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.

The objective draw by this project is to develop tools for Plug-in Hybrid Electric Vehicle (PHEV) design, energy analysis and energy management, with the aim of analyzing the effect of design, driving cycles, charging frequency and energy management on performance, fuel economy, range and battery life. A Chevrolet Equinox fueled by bio diesel B20 has been hybridized at the Center for Automotive Research (CAR), at The Ohio State University. The vehicle model has been developed in Matlab/Simulink environment, and validated based on laboratory and test. The PHEV battery pack has been modeled starting from Li-Ion batteries experimental data and then implemented into the simulator. In order to simulate “real world” scenarios, custom driving cycles/typical days were identified starting from average driving statistics and well-known cycles.

Electronic throttle control is increasingly being considered as a viable alternative to conventional air management systems in modern spark-ignition engines. In such a scheme, driver throttle commands are interpreted by the powertrain control module together with many other inputs; rather than directly commanding throttle position, the driver is now simply requesting torque - a request that needs to be appropriately interpreted by the control module. Engine management under these conditions will require optimal control of the engine torque required by the various vehicle subsystems, ranging from HVAC, to electrical and hydraulic accessories, to the vehicle itself. In this context, the real-time estimation of engine and load torque can play a very important role, especially if this estimation can be performed using the same signals already available to the powertrain control module.

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.

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.

Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

Engine knock has been recognized as a major problem limiting the development of fuel efficient spark-ignition engines. Detection methods employed in current knock control systems for spark ignition engines use a measurement of engine block vibration tuned to one or more resonance frequencies to extract knock-related information from the engine structural vibration. A major problem in the detection of knock (especially at higher engine speed) in commercial engines is the isolation of the desired signal from the contributions of the components other than those associated with the phenomenon under investigation. This is generally referred to as background noise. It is known that the engine knock resonance frequencies vary due to changes in combustion chamber volume and temperature during the expansion phase. Therefore, we propose an improved knock detection method using joint time-frequency analysis of engine block vibration and pressure signals.