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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.

As vehicle development timelines continue to shorten, it is necessary for the full vehicle NVH engineer to be able to predict performance without actual prototypes. There has been significant advancement in the accuracy of finite element modeling techniques of trimmed bodies; however accuracy is still low in the road noise mid frequency range from 150-400Hz. Also, calculation times for these frequencies are long, with very large results files in some cases. To alleviate these limitations, a Hybrid approach has been used, where a finite element suspension and drive train model is coupled with a test based Frequency Response Function (FRF) model of the trimmed body. The predicted road noise level was compared to actual vehicle tests and exhibited excellent correlation.

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 last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

During Phase VI of the National Highway Traffic Safety Administration's (NHTSA) Light Vehicle Rollover Research Program, three of the twenty-six light vehicles tested exhibited significant response asymmetries with respect to left versus right steer maneuvers. This paper investigates possible vehicle asymmetric characteristics and unintended inputs that may cause vehicle asymmetric response. An analysis of the field test data, results from suspension and steering parameter measurements, and a summary of a computer simulation study are also given.

The mixture formation processes of methane and air in an optical access engine operating steadily at 200 RPM have been explored in order to study charge inhomogeneity in a natural gas powered spark ignition engine during transient engine cranking. Planar Laser Induced Fluorescence has been used to create fuel/air equivalence ratio maps as a function of injection timing for various image planes at intervals throughout the intake and compression strokes. The work has been done using a Honda VTEC-E engine head that features port injection, four valves per cylinder, a pentroof style combustion chamber for the generation of tumble motion, and one nearly deactivated intake valve to generate swirl motion at low engine speeds in order to enhance mixing.

The rapid development of driver assistance systems, such as lane-departure warning (LDW) and lane-keeping support (LKS), along with widely publicized reports of automated vehicle testing, have created the expectation for an increasing amount of vehicle automation in the near future. As these systems are being phased in, the coexistence of automated vehicles and human-driven vehicles on roadways will be inevitable and necessary. In order to develop automated vehicles that integrate well with those that are operated in traditional ways, an appropriate understanding of human driver behavior in normal traffic situations would be beneficial. Unlike many research studies that have focused on collision-avoidance maneuvering, this paper analyzes the behavior of human drivers in response to cut-in vehicles moving at similar speeds. Both automated and human-driven vehicles are likely to encounter this scenario in daily highway driving.

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.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

Automating road vehicle control can increase the range and reliability of dynamic testing. Some tests, for instance, specify precise steering inputs which human test drivers are only able to approximate, adding uncertainty to the test results. An automated steering system has been developed which is capable of removing these limitations. This system enables any production car or light truck to follow a user-defined path, using global position feedback, or to perform specific steering sequences with excellent repeatability. The system adapts itself to a given vehicle s handling characteristics, and it can be installed and uninstalled quickly without damage or permanent modification to the vehicle.

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.

The current Si/polymeric medical diagnostic sensors that are on the market only feature a one-point calibration system [1]. Such a measurement results in less accurate sensing and more in-factory sensor rejection. The two-point calibration fluidic method introduced here will alleviate some of the shortcomings of such current miniature analytical systems. Our fluidic platform is a disposable, multi-purpose micro analytical laboratory on a compact disc (CD) [2, 3]. This system is based on the centrifugal force, in which fluidic flow can be controlled by the spinning rate of the CD and thus a whole range of fluidic functions including valving, mixing, metering, splitting, and separation can be implemented. Furthermore, optical detection such as absorption and fluorescence can be incorporated into the CD control unit to obtain signals from pre-specified positions on the disc.

Dynamic Brake Support (DBS) is a safety system that has been applied to various passenger cars and has been shown to be effective at assisting drivers in avoiding or mitigating rear-end collisions. The objective of a DBS system is to ensure that the brake system is applied quickly and at sufficient pressure when a driver responds to a collision imminent situation. DBS is capable of improving braking response due to a passenger car driver's tendency to utilize multi-stage braking. Interest is developing in using DBS on commercial vehicles. In order to evaluate the possible improvement in safety that could be realized through the use of DBS, driver braking behavior must first be analyzed to confirm that improvement is possible and necessary. To determine if this is the case, a study of the response of truck drivers' braking behavior in collision imminent situations is conducted. This paper presents the method of evaluation and results.

This paper describes the procedures used to reduce the tonal noise of a class eight truck engine timing gear train that was initially found to be objectionable under idle operating conditions. Initial measurements showed that the objectionable sounds were related to the fundamental gear mesh frequency, and its second and third harmonics. Experimental and computational procedures used to study and trouble-shoot the problem include vibration and sound measurements, transmission error analysis of the gears under light load condition, and a dynamic analysis of the drive system. Detail applications of these techniques are described in this paper.

During a motor-vehicle collision, an occupant may interact with a variety of interior structures. The material properties and construction of these structures can directly affect the occupant's kinetic response. Simulation tools such as MADYMO (Mathematical Dynamical Models) can be used to estimate the forces imparted to an occupant for injury mechanism and causation evaluation relative to a particular event. Depending on the impact event and the specific injury mechanism being evaluated, the selection of proper material characteristics can be quite important. A comprehensive literature review of MADYMO studies illustrates the prevalent use of generic material characteristics and the need for improved property estimation and implementation methods.

This paper presents the details of the model for the physical steering system used on the National Advanced Driving Simulator. The system is basically a hardware-in-the-loop (steering feedback motor and controls) steering system coupled with the core vehicle dynamics of the simulator. The system's torque control uses cascaded position and velocity feedback and is controlled to provide steering feedback with variable stiffness and dynamic properties. The reference model, which calculates the desired value of the torque, is made of power steering torque, damping function torque, torque from tires, locking limit torque, and driver input torque. The model also provides a unique steering dead-band function that is important for on-center feel. A Simulink model of the hardware/software is presented and analysis of the simulator steering system is provided.

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.

Having connectivity among ground vehicles brings about benefits in fuel economy improvement, traffic mobility enhancement and undesired emission reductions. On the other hand, Unmanned Aerial Vehicles (UAV) have proven to help in getting aerial data to end users in an affordable manner. When UAVs are equipped with cameras, they can get information about the terrain they are flying over. Moreover, using Vehicle-to-Everything (V2X) communication technologies, it is possible to form a communication link between UAVs and the connected ground vehicle networks comprising of Connected and Autonomous vehicles (CAVs). To investigate and exploit the potential benefits and use cases of a broad vehicle network, a microscopic traffic simulator modified previously by our group with the addition of nearby UAVs is used to integrate simulated Connected UAVs flying above a realistic simulation of heterogeneous traffic flow containing both CAVs and non-CAVs.

A test procedure that rates brake performance must control variability so that measured differences between vehicles are real. Tests were conducted using standard brake test procedures with three drivers in three cars on wet and dry asphalt with the ABS working and disabled. The differences between vehicles were greater than differences due to ABS condition, surface condition, and drivers. The procedure measured differences between all the vehicles with statistical certainty but used many replications and drivers. If only large differences in performance need to be distinguished, fewer replications and drivers will be needed.

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output.