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The paper presents a possible concept design for integration of individual wheel AC motors into Oshkosh Truck Corporation's InDependent Suspension. A new axle concept design (including drive line and CV-joint) is presented with a new AC induction motor concept. Both concepts are able to match 100% the sever-heavy duty requirements in a large area of advanced on and off road traction applications. Concepts are suitable for modularity in a multi-axle (2-6) All-Wheel Drive, All Steer configuration vehicle.

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.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

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.

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.

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.

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 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.

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.

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.