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Carbon canisters have been adapted for automobile use since the early 1970s to control evaporative emissions. Stringent emission regulations and the requirement for an enhanced evaporative emissions test procedure, make this an important issue. The air and evaporative fuel from the carbon canister therefore need important consideration with respect to air to fuel ratio (AFR) control and idle by-pass air control. Although a few complex models of the activated carbon canister have been developed, a control-oriented, simplistic carbon canister model needs to be developed. This paper explores the control-oriented modeling of a canister purge air system along with the on-line estimation of evaporative fuel loading of the activated carbon. An attempt was made at providing an analytical expression for the evaporative fuel and air entering the intake manifold.

There is increasing interest in the use of alternative fuels for transportation, due to the increasing cost of petroleum based fuels. One possible alternative to the use of petroleum for transportation is to use electric grid power. This paper explores a possible design solution based on a plug-in fuel cell hybrid. A scaled down version of FC-HEV that is applicable to this concept, has been implemented as a proof of concept with fast prototyping toolkits, including a 32 bit micro processor, Matlab/Simulink software and an embedded system development kit. The resulting prototype vehicle demonstrated a high gasoline equivalent MPG as well as a successful functionality of micro grid power generation.

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.

Due to the stringent emission regulations, On-Board Diagnostics II (OBD II) and the requirement of enhanced evaporative emissions test procedure, an aggressive canister purge control strategy is required for automotive vehicles. The enhanced evaporative emissions test procedure has forced car manufacturer to purge the carbon canister in the vehicle idle condition so that production vehicles meet the SHED and hot soak test requirements. This not only worsens the idle speed quality but also tends to increase exhaust emission levels. Using analytical models of evaporative air and fuel, feed-forward control strategy for both idle by-pass air and air to fuel ratio can be improved. This paper demonstrates an application of evaporative system modeling to the idle air and air to fuel ratio control.

For simulation and analysis of vehicles there is a need to have a means of generating drive cycles which have properties similar to real world driving. A method is presented which uses measured vehicle speed from a number of vehicles to generate a Markov chain model. This Markov chain model is capable of generating drive cycles which match the statistics of the original data set. This Markov model is then used in an iterative fashion to generate drive cycles which match constraints imposed by the user. These constraints could include factors such number of stops, total distance, average speed, or maximum speed. In this paper, systematic analysis was done for a PHEV fleet which consists of 9 PHEVs that were instrumented using data loggers for a period of approximately two years. Statistical analysis using principal component analysis and a clustering approach was carried out for the real world velocity profiles.

Building and testing of physical prototypes for optimization purposes consume significant amount of time, manpower and financial resources. Mathematical formulation and solution of vehicle multibody dynamics equations are also not feasible because of the massive size of the problem. This paper proposes a methodology for vehicle design optimization that does not involve physical prototyping or exhaustive mathematics. The proposed method is fast, cost effective and saves considerable manpower. The methodology uses an industry acknowledged multibody dynamics simulation software (ADAMS) and a flexible architecture to explore large design spaces.

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.

A methodology has been developed to generate military vehicle driving cycles for use in vehicle simulation models. This methodology is based upon the mission profile for a vehicle, which is typically given within a vehicle's specifications and lists the types of terrains that the vehicle is likely to encounter. A simplistic vehicle powertrain and road load model and the Bekker vehicle-soil interaction model are used to estimate the vehicle performance over each type of terrain. Two types of driving cycles are generated within a Graphical User Interface developed within MATLAB using the results of the vehicle models: Linear modes driving cycles, and Real-world driving cycles.

This paper describes the development and experimental validation of a Plug-in Hybrid Electric Vehicle (PHEV) dynamic simulator that enables development, testing, and calibration of a traction control strategy. EcoCAR 2 is a three-year competition between fifteen North American universities, sponsored by the Department of Energy and General Motors that challenges students to redesign a Chevrolet Malibu to have increased fuel economy and decreased emissions while maintaining safety, performance, and consumer acceptability. The dynamic model is developed specifically for the Ohio State University EcoCAR 2 Team vehicle with a series-parallel PHEV architecture. This architecture features, in the front of the vehicle, an ICE separated from an automated manual transmission with a clutch as well as an electric machine coupled via a belt directly to the input of the transmission. The rear powertrain features another electric machine coupled to a fixed ratio gearbox connected to the wheels.

We have performed an extensive study of cycle-to-cycle variation of engine knock and its occurrence in order to have a better understanding of engine knock. Experimental studies show the randomness of knock as well as its frequency varying nature. We propose using time-frequency based signal detection method to improve knock detection since this method can track frequency variations in the measured signals. The fundamental idea behind time-frequency analysis is to be able to understand and describe how the spectral content of a signal is changing in time. Classical signal analysis has traditionally dealt with time and frequency separately. Such individual descriptions are good in situation where spectral content of signals do not change with time; however, there are often signals that have time-varying spectral content such as engine knock. It is shown that one gains more information about knock signals using time-frequency analysis method.

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.

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.

This paper discusses the use of intelligent control techniques for the control of a parallel hybrid electric vehicle powertrain. Artificial neural networks and fuzzy logic are used to implement a load leveling strategy. The resulting vehicle control unit, a supervisory controller, coordinates the powertrain components. The presented controller has the ability to adapt to different drivers and driving cycles. This allows a control strategy which includes both fuel-economy and performance modes. The strategy was implemented on the Ohio State University FutureCar.

This paper presents the results of a design space exploration based on the simulations of the MTV (Medium Tactical Vehicle) 5.0 Ton Cargo Truck using MSC-ADAMS (Automatic Dynamic Analysis of Mechanical System). Design space study is conducted using ADAMS/Car and ADAMS/Insight to consider parametric design changes in suspension and the tires of the cargo truck. The methodology uses an industry acknowledged multibody dynamics simulation software (ADAMS) for the modeling of the cargo truck and a flexible optimization architecture to explore the design space. This research is a part of the work done for the U.S. Army TACOM (Tank Automotive and Armaments Command) at the Center for Automotive Research, The Ohio State University.

This paper describes the background and development of a program focused on motorsports engineering education currently in progress at the Ohio State University (OSU). An interdisciplinary curriculum, with the involvement of various engineering departments, is being proposed for development in an attempt to address some of the engineering education needs of the motorsports industry. The program described in this paper strives to provide engineering students with an interdisciplinary background race engineering, and also provides opportunities for motorsports oriented thesis projects. The paper briefly summarizes the key elements of the curriculum, and describes how the integration of course material from different disciplines with team work on student competition projects, possibly coupled with internships with racing teams, can provide an ideal setting for the education of a new generation of race engineers.

Fuel economy, performance and driveability are three important subjects for evaluating vehicle performance. Evaluations in both simulations and real vehicles prefer objective and quantitative measures. Subjective and descriptive metrics cannot be easily implemented in simulations, and these evaluations vary with changing time or evaluators. Fuel economy is usually estimated under various city, highway and some other user-defined driving cycles. Performance criteria consist of acceleration/deceleration performance, gradeability and towing capability. Driveability measures deal with pedal responsiveness, operating smoothness and driving comfort. This includes interior noise level, jerk and acceleration parameters. Numerical references and some interpretations of the above metrics are presented in this paper, as well as how these metrics can be used to evaluate vehicle powertrain design and control strategy development.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

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 44 miles of all-electric range. The vehicle features an 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 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper details three years of modeling and simulation development for the OSU EcoCAR 2 vehicle. Included in this paper are the processes for developing simulation platform and model requirements, plant model and soft ECU development, test development and validation, automated regression testing, and controls and calibration optimization.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

The EcoCAR 2 team at the Ohio State University has designed an extended-range electric vehicle capable of 44 miles all-electric range, which 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 a 6-speed automated manual transmission. This vehicle is designed to reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report documents the team's refinement work on the vehicle during Year 3 of the competition, including vehicle improvements, control strategy calibration and dynamic vehicle testing, culminating in a 99% buy off vehicle that meets the goals set forth by the team. This effort was made possible through support from the U.S. Department of Energy, General Motors, The Ohio State University, and numerous competition and local sponsors.