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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 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 integrity of the exhaust emission system in a passenger vehicle can best be maintained by monitoring its performance continuously on board the vehicle. It is with the intent of monitoring emission system performance that the California Air Resources Board has proposed regulations which will require vehicles to be equipped with on-board monitoring systems. These proposed regulations are known as OBDII and will probably be followed by similar Federal EPA regulations.This paper discusses a method of monitoring engine misfire as part of the OBDII requirements for passenger vehicle on-board diagnostics. The method is relatively inexpensive in that it uses an existing sensor for measuring instantaneous crankshaft angular position, and utilizes electronic signal processing which can be implemented in relatively inexpensive custom integrated circuits.

This paper discusses an improved design of a vehicle-based mobile off-road terrain profile measurement system. The proposed system includes an apparatus of sensors and on-board data acquisition hardware, equipped on a platform vehicle used to measure and record the relevant data while the vehicle travels through the off-road or terrain surface to be surveyed. A unique post-processing algorithm is then used to derive the elevation profile based on the collected data. The derived elevation profile data could be used to characterize the roughness of an off-road testing course or perform a general geographical survey or mapping. The major technical issue addressed in this system is to eliminate the effect of platform vehicle vibration on sensor measurement which if left unaddressed will result in large measurement error due to high amplitude pitch and roll movements of the platform vehicle.

Published NHTSA rulemaking plans propose significant reduction in the maximum stopping distance for loaded Class-VIII commercial vehicles. To attain that goal, higher torque brakes, such as air disc brakes, will appear on prime movers long before the trailer market sees significant penetration. Electronic control of the brakes on prime movers should also be expected due to their ability to significantly shorten stopping distances. The influence upon jackknife stability of having higher performance brakes on the prime mover, while keeping traditional pneumatically controlled s-cam drum brakes on the trailer, is discussed in this paper. A hybrid vehicle dynamics model was applied to investigate the jackknife stability of tractor-semitrailer rigs under several combinations of load, speed, surface coefficient, and ABS functionality.

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.

This paper discusses the derivation and validation of planar models of articulated vehicles that were developed to analyze jackknife stability on low-μ surfaces. The equations of motion are rigorously derived using Lagrange's method, then linearized for use in state-space models. The models are verified using TruckSim™, a popular nonlinear solid body vehicle dynamics modeling package. The TruckSim™ models were previously verified using extensive on-vehicle experimental data [1, 2]. A three-axle articulated model is expanded to contain five axles to avoid lumping the parameters for the drive and semitrailer tandems. Compromises inherent in using the linearized models are discussed and evaluated. Finally, a nonlinear tire cornering force model is coupled with the 5-axle model, and its ability to simulate a jackknife event is demonstrated. The model is shown to be valid over a wide range of inputs, up to and including loss of control, on low-and-medium-μ surfaces.

The widely used Extended Kalman Filter (EKF) is applied to a planar model of an articulated vehicle to predict jackknifing events. The states of hitch angle and hitch angle rate are estimated using a vehicle model and the available or “measured” states of lateral acceleration and yaw rate from the prime mover. Tuning, performance, and compromises for the EKF in this application are discussed. This application of the EKF is effective in predicting the onset of instability for an articulated vehicle under low-μ and low-load conditions. These conditions have been shown to be most likely to render heavy articulated vehicles vulnerable to jackknife instability. Options for model refinements are also presented.

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.

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.

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.

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.

This paper presents an all-wheel-drive (AWD) hybrid electric vehicle (HEV) design approach for extreme off-road applications. The paper focuses on the powertrain design, modeling, simulation, and performance analysis. Since this project focuses on a military-type application, the powertrain is designed to enhance crew survivability and provide several different modes of limp-home operation by utilizing a new vehicle topology -herein referred to as the island topology. This topology consists of designing the vehicle such that the powertrain and other equipment and subsystems surround the crew compartment to provide a high level of protection against munitions and other harmful ordnance. Thus, in the event of an external shield penetration, the crew compartment remains protected by the surrounding equipment - which serves as a secondary shield.

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 introduction of advanced electronic controls in passenger vehicles over the last decade has made traditional diagnostic methods inadequate to satisfy on- and off-board diagnostic needs. Due to the complexity of today's automotive control systems, it is imperative that appropriate diagnostic tools be developed that are capable of satisfying current and projected service and on-board requirements. The performance of available diagnostic and test equipment is still amenable to further improvement, especially as it pertains to the diagnosis of incipient and intermittent faults. It is our contention that significant improvement is possible in these areas. This paper briefly summarizes the evolution of on- and off-board diagnostic tools documented in the published literature, with the aim of giving the reader an understanding of their capabilities and limitations, and it further proposes alternative solutions that may be adopted as a basis for an advanced diagnostic instrument.

Legislation pertaining to automobile emissions has caused an increased focus on the cold-start performance of internal combustion engines. Of particular concern is the period of time before all available sensors become active. Present engine control strategies must rely on methods other than feedback control while these sensors are not active. Without feedback control during this critical period, engine emissions performance is not optimized. These conditions cause difficulty in performing comprehensive cold-start experiments. For these reasons, we have developed several methods for feedback control during cold-start to aid in laboratory investigations of engine emissions phenomena.

An approach to fault diagnosis for internal combustion engines is considered. It is based on the estimation of cylinder indicated torque by means of sliding mode observers. Instead of measuring indicated pressure in cylinders directly, crankshaft speed is measured as the input of observers, which estimate the indicated torque. Several engine models are considered with different levels of complexity. The indicated torque estimation using sliding mode observers is based on the equivalent control method. The estimation technique is validated experimently on a research engine.

In recent years considerable interest has been placed on the detection of engine misfire. As part of the California Air Resources Board on-board diagnostics regulations for 1994 model year vehicles, misfire should be monitored continuously by the engine diagnostic system. It is expected that the next generation of on-board diagnostics regulations will demand monitoring of partial misfire as well. Several solutions to the misfire detection problem have been proposed and demonstrated for the detection of complete misfires. However, the performance of these methods in the presence of partial misfire is not altogether clear. The aim of this paper is to evaluate the performance of various misfire detection indices, all based on a measurement of crankshaft angular velocity, in the presence of partial misfire. The proposed algorithms are compared to a standard based on a measurement of indicated pressure.

In this paper, we propose an IC engine fuel system diagnostic algorithm based on a discrete-event nonlinear observer using the production oxygen sensor. A mean value engine model is used to describe the engine dynamics. A procedure for designing the discrete event based observer is presented and applied to estimate important engine variables using the measured binary oxygen sensor output. The estimated variables are then used to perform diagnostics of the fuel system of the IC engine. Experimental results on a multi-cylinder production engine are presented to demonstrate the effectiveness of the proposed method.