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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 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.

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.

This paper presents the implementation and performance of an electric all-wheel drive system on a series-parallel, through-the-road hybrid electric vehicle. Conventional methods of all-wheel drive do not provide a suitable solution for this type of vehicle as the powertrain lacks a mechanical link between the front and rear axles. Moreover, this unique architecture allows the vehicle to be propelled solely by the front, or the rear, wheels during typical operation. Thus, the algorithm presented here manages wheel slip by either the front, or rear wheels when engaging to provide all-wheel drive capability. necessary testing validates the robustness of this Extensive system.

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.

This paper presents the design and development of a high-power, high-speed “road train” (with both on- and off-road applications). The system looks to optimize both high-speed operation and low-speed, close-quarters driving with the introduction of autonomous power modules. Each trailer in the road train has it own electric traction system. When driving on open roads or in open areas, each traction system receives electric energy from the high-powered tractor. However, the individual traction systems allow for distributed tractive effort, improving upon the classic road train. Further, each module has its own independent steering system, allowing for practical implementation of longer trains. Use of longer trains in open areas allows for reduced operational costs, and increased efficiency. When mobility becomes a primary concern or zero emissions operation is needed, small power supplies can allow independent trailer operation.

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.

The paper presents a possible concept design for integration of individual wheel AC motors into Oshkosh Truck Corporation's InDependent Suspension. A new axle concept design (including drive line and CV-joint) is presented with a new AC induction motor concept. Both concepts are able to match 100% the sever-heavy duty requirements in a large area of advanced on and off road traction applications. Concepts are suitable for modularity in a multi-axle (2-6) All-Wheel Drive, All Steer configuration vehicle.

This paper presents the conceptual design of a high-power, high-speed tractor-trailer for severe duty applications. Design of the tractor-trailer introduces several new concepts, including the general vehicle architecture, a new electrical transmission system and a new electric tandem axle. The vehicle architecture consists of a low drag cab concept with a fully integrated turbo-generator power source, an exhaust gas electric decontamination system and auxiliaries. The electric transmission introduces a new combination of electrical machines and power electronics designed to perform under maximum load with minimum dimension, weight and price. The electric tandem axle is a new concept of an all-wheel steering independent suspension with virtual electromagnetic differential.

This paper introduces a new series of empirical mathematical models developed to characterize brake torque generation of pneumatically actuated Class-8 vehicle brakes. The brake torque models, presented as functions of brake chamber pressure and application speed, accurately simulate steer axle, drive axle, and trailer tandem brakes, as well as air disc brakes (ADB). The contemporary data that support this research were collected using an industry standard inertia-type brake dynamometer, routinely used for verification of FMVSS 121 commercial vehicle brake standards.

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.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

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.

Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

Crankshaft angular position measurements are fundamental to all modern automotive engines. These measurements are required to control fuel injection timing as well as ignition timing. However, many other functions can be performed from such measurements through the use of advanced signal processing. These additional functions are essentially diagnostic in nature although there is potential for substitution of primary fuel and ignition control functions. This paper illustrates the application of crankshaft angular position measurement to the estimation of individual cylinder indicated and/or brake torque in IC engines from measurement of crankshaft position/velocity.

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.

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.

Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. A major concern of these vehicles is how to effectively operate the electric machine and the ICE. Towards this end two operation strategies, an best efficiency and a least fuel use strategy, are presented in this paper. To demonstrate the potential of an advanced operation strategy for HEV's, a fuzzy logic controller has been developed and implemented in simulation in the National Renewable Energy Laboratory's simulator Advisor (version 2.0.2). Results have also been gathered from chassis dynamometer tests in order to verify the effectiveness of Advisor. The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy.

A computer simulation has been developed that models conventional, electric, and hybrid drivetrains. The vehicle's performance is predicted for a given driving cycle, such as the Federal Urban Driving Schedule (FUDS). This computer simulation was used in a massive designspace exploration to simulate 1.8 million different vehicles, including conventional, electric, and hybrid-electric vehicles (HEVs). This paper gives a description of the vehicle simulator as well as the results and implications of the large design-space exploration.