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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.

To accurately evaluate the energy consumption benefits provided by connected and automated vehicles (CAV), it is necessary to establish a reasonable baseline virtual driver, against which the improvements are quantified before field testing. Virtual driver models have been developed that mimic the real-world driver, predicting a longitudinal vehicle speed profile based on the route information and the presence of a lead vehicle. The Intelligent Driver Model (IDM) is a well-known virtual driver model which is also used in the microscopic traffic simulator, SUMO. The Enhanced Driver Model (EDM) has emerged as a notable improvement of the IDM. The EDM has been shown to accurately forecast the driver response of a passenger vehicle to urban and highway driving conditions, including the special case of approaching a signalized intersection with varying signal phases and timing. However, most of the efforts in the literature to calibrate driver models have focused on passenger vehicles.

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.

This paper presents a sensitivity analysis-based study aimed at robustly calibrating the parameters of an adaptive energy management strategy designed for a Plugin Hybrid Electric Vehicle (PHEV). The supervisory control is developed from the Pontryagin's Minimum Principle (PMP) approach and applied to a model of a GM Chevrolet Volt vehicle. The proposed controller aims at minimizing the fuel consumption of the vehicle over a given driving mission, by achieving a blended discharge strategy over the entire cycle. The calibration study is conducted over a wide set of driving conditions and it generates a look-up table and two constant values for the three controller parameters to be used in the in-vehicle implementation. Finally, the calibrated adaptive control strategy is validated against real driving cycles showing the effectiveness of the calibration approach.

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.

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.

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.

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.

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.

Research has been conducted to develop a methodology for the generation of driving and duty cycles for refuse vehicles in conjunction with a larger effort in the design of a hybrid-electric refuse vehicle. This methodology includes the definition of real-world data that was collected, as well as a data analysis procedure based on sequencing of the collected data into micro-trips and hydraulic cycles. The methodology then applies multi-variate statistical analysis techniques to the sequences for classification. Finally, driving and duty cycles are generated based on matching the statistical metrics and distributions of the generated cycles to the collected database. Simulated vehicle fuel economy for these cycles is also compared to measured values.

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.

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.

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.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

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.