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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.

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 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.

This paper presents the application of detection filters to the diagnosis of sensor and actuator failures in automotive control systems. The detection filter is the embodiment of a model-based failure detection and isolation (FDI) methodology, which utilizes analytical redundancy within a dynamical system (e.g., engine/controller) to isolate the cause and location of abnormal behavior (i.e., failures). The FDI methodology has been used, among other applications, in the aerospace industry for fault diagnosis of inertial navigation systems and flight controllers. This paper presents the philosophy and essential features of FDI theory, and describes the practical application of the method to the diagnosis of faults in the throttle position sensor in an electronically controlled IC engine. The paper also discusses the incorporation of FDI systems in the design process of a control strategy, with the aim of increasing reliability by embedding diagnostic features within the control strategy.

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.

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.

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 introduces a new nonlinear model for simulating the dynamics of pneumatic-over-mechanical commercial vehicle braking systems. The model employs an effective systems approach to accurately reproduce forcing functions experienced at the hubs of heavy commercial vehicles under braking. The model, which includes an on-off type ABS controller, was developed to accurately simulate the steer, drive, and trailer axle drum (or disc) brakes on modern heavy commercial vehicles. This model includes parameters for the pneumatic brake control and operating systems, a 4s/4m (four sensor, four modulator) ABS controller for the tractor, and a 2s/2m ABS controller for the trailer. The dynamics of the pneumatic control (treadle system) are also modeled. Finally, simulation results are compared to experimental data for a variety of conditions.

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.

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.

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket.

This paper presents the real time implementation of detection filters for the diagnosis of incipient failures in electronically controlled internal combustion (IC) engines. The detection filters are implemented in a production vehicle. Recent results [1] have demonstrated the feasibility of a model-based failure detection and isolation (FDI) methodology for detecting partially failed components in electronically controlled vehicle subsystems. The present paper describes the real time application of the FDI concept to the detection of faults in sensors associated with the engine/controller In a detection filter, the performance of the engine/controller system is continuously compared to a prediction based on sensor measurements and an analytical model (typically a control model) of the system. Any discrepancy between actual and predicted performance is analyzed to identify the unique failure signatures related to specific system components.

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.