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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.

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.

Over the years numerous researchers have suggested that the ionization current signal carries within it combustion relevant information. The possibility of using this signal for diagnostics and control provides motivation for continued research in this area. To be able to use the ion current signal for feedback control a reliable estimate of some combustion related parameter is necessary and therein lies the difficulty. Given the nature of the ion current signal this is not a trivial task. Fei An et al. [1] employed PCA for feature extraction and then used these feature vectors to design a neural network based classifier for the estimation of air to fuel ratio (AFR). Although the classifier predicted AFR with sufficient reliability, a major draw back was that the ion current signals used for prediction were averaged signals thus precluding a cycle to cycle estimate of AFR.

This paper presents the development of a model-based air/fuel ratio controller for a high performance engine that uses, in addition to other usual signals, the throttle angle to enable predictive air mass flow rate estimation. The objective of the paper is to evaluate the possibility to achieve a finer air/fuel ratio control during transients that involve sudden variations in the physical conditions inside the intake manifold, due, for example, to fast throttle opening or closing actions. The air mass flow rate toward the engine cylinders undertakes strong variation in such transients, and its correct estimation becomes critical mainly because of the time lag between its evaluation and the instant when the air actually enters the cylinders.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

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.

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.

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.

Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer.

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.

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.

Electronic throttle control is increasingly being considered as a viable alternative to conventional air management systems in modern spark-ignition engines. In such a scheme, driver throttle commands are interpreted by the powertrain control module together with many other inputs; rather than directly commanding throttle position, the driver is now simply requesting torque - a request that needs to be appropriately interpreted by the control module. Engine management under these conditions will require optimal control of the engine torque required by the various vehicle subsystems, ranging from HVAC, to electrical and hydraulic accessories, to the vehicle itself. In this context, the real-time estimation of engine and load torque can play a very important role, especially if this estimation can be performed using the same signals already available to the powertrain control module.

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.

Electronic Throttle Control systems substitute the driver in commanding throttle position, with the driver acting on a potentiometer connected to the accelerator pedal. Such strategies allow precise control of air-fuel ratio and of other parameters, e.g. engine efficiency or vehicle driveability, but require detailed information about the engine operating conditions, in order to be implemented inside the Electronic Control Unit (ECU). In order to determine throttle position, an interpretation of the driver desire (revealed by the accelerator pedal position) is performed by the ECU. In our approach, such interpretation is carried out in terms of a torque request that can be appropriately addressed knowing the actual engine-vehicle operating conditions, which depend on the acting torques. Estimates of the torque due to in-cylinder pressure (indicated torque), as well as the torque required by the vehicle (load torque), must then be available to the control module.

The viability of many new technologies for improving the drivability and safety of a vehicle has improved with the availability of advanced software and hardware tools. On-line diagnosis of steering system faults is one such area on which a lot of attention has been focused. When used in a manually driven automobile this technology can improve the safety of the vehicle by providing the driver with the fault information. While when used with a computer controlled steering (as envisaged in many of the IVHS technologies) it is of even greater importance, because electronic fault information is crucial to the proper functioning of many such systems. This paper deals with the design of a linear unknown input observer (UIO) based residual generator for steering system diagnosis. The observer was designed based on an accepted model of the automatic car steering problem. The observer was validated through experiments conducted on the OSU-autonomous vehicle.

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.

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.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting more attention in the automotive industry with the technology improvement and increasing focus on fuel economy. For EVs and HEVs, especially all-wheel drive (AWD) EVs with two electric motors powering front and rear axles separately, an accurate motor speed measurement through resolver is significant for vehicle performance and drivability requirement, subject to resolver faults including amplitude imbalance, quadrature imperfection and reference phase shift. This paper proposes a diagnostic scheme for the specific type of resolver fault, amplitude imbalance, in AWD EVs. Based on structural analysis, the vehicle structure is analyzed considering the vehicle architecture and the sensor setup. Different vehicle drive scenarios are studied for designing diagnostic decision logic. The residuals are designed in accordance with the results of structural analysis and the diagnostic decision logic.