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This paper and the associated lecture present an overview of technology trends and of market and business opportunities created by technology, as well as of the challenges posed by environmental and economic considerations. Commercial vehicles are one of the engines of our economy. Moving goods and people efficiently and economically is a key to continued industrial development and to strong employment. Trucks are responsible for nearly 70% of the movement of goods in the USA (by value) and represent approximately 300 billion of the 3.21 trillion annual vehicle miles travelled by all vehicles in the USA while public transit enables mobility and access to jobs for millions of people, with over 10 billion trips annually in the USA creating and sustaining employment opportunities.

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.

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.

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.

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.

The intent of this research is to understand the effects worn dampers have on vehicle stability and safety through dynamic model simulation. Dampers, an integral component of a vehicle's suspension system, play an important role in isolating road disturbances from the driver by controlling the motions of the sprung and unsprung masses. This paper will show that a decrease in damping leads to excessive body motions and a potentially unstable vehicle. The concept of poor damping affecting vehicle stability is well established through linear models. The next step is to extend this concept for non-linear models. This is accomplished through creating a vehicle simulation model and executing several driving maneuvers with various damper characteristics. The damper models used in this study are based on splines representing peak force versus velocity relationships.

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 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.

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.

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.

The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present.

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.

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.

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.

The aim of this paper is to describe a unified approach to modeling the energy efficiency and power flow characteristics of energy storage and energy conversion elements used in hybrid vehicles. Hybrid vehicle analysis and design is concerned with the storage of energy in three domains - chemical, mechanical, and electrical - and on energy conversions between these domains. The paper presents the physical and mathematical basis of this modeling approach, as well as a modular simulator that embodies the same basic principles. The use of the simulator as an analysis tool is demonstrated through the conceptual design of a sport-utility hybrid drivetrain.

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.

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.

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.

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.