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Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

As the new features for driver assistance and active safety systems are growing rapidly in vehicles, the simulation within a virtual environment has become a necessity. The current active safety system consists of Electronic Control Units (ECUs) which are coupled to camera and radar sensors. Two methods of implementation exists, integrated sensors with control modules or separation of sensors form control modules. The subsystem integration testing poses new challenges for virtual environment for simulation of active safety features. The comprehensive simulation environment for integration testing consists of chassis controls, powertrain, driver assistance, body and displays controllers. Additional complexity in the system is the serial communication strategy. Multiple communication protocols such as GMLAN, LIN, standard CAN, and Flexray could be present within the same vehicle topology.

Forward Collision Alert (or Forward Collision Warning) systems provide alerts intended to assist drivers in avoiding or mitigating the harm caused by rear-end crashes. These systems currently use front-grille mounted, forward-looking radar devices as the primary sensor. In contrast, Lane Departure Warning (LDW) systems employ forward-looking cameras mounted behind the windshield to monitor lane markings ahead and warn drivers of unintended lane violations. The increasing imaging sensor resolution and processing capability of forward-looking cameras, as well recent important advances in machine vision algorithms, have pushed the state-of-the-art for camera-based features. Consequently, camera-based systems are emerging as a key crash avoidance system component in both a primary and supporting sensing role. There are currently no production vehicles with cameras used as the sole FCA sensing device.

Fuel economy improvement and stringent emission regulations worldwide require advanced air charging and combustion technologies, such as low temperature combustion, PCCI or HCCI combustion. Furthermore, NOx aftertreatment systems, like Selective Catalyst Reduction (SCR) or lean NOx trap (LNT), are needed to reduce vehicle tailpipe emissions. The information on engine-out NOx emissions is essential for engine combustion optimization, for engine and aftertreatment system development, especially for those involving combustion optimization, system integration, control strategies, and for on-board diagnosis (OBD). A physical NOx sensor involves additional cost and requires on-board diagnostic algorithms to monitor the performance of the NOx sensor.

This paper discusses observability of the vehicle states using different sensor configurations as well as fault-tolerant estimation of these states. The optimality of the sensor configurations is assessed through different observability measures and by using a 3-DOF linear vehicle model that incorporates yaw, roll and lateral motions of the vehicle. The most optimal sensor configuration is adopted and an observer is designed to estimate the states of the vehicle handling dynamics. Robustness of the observer against sensor failure is investigated. A fault-tolerant adaptive estimation algorithm is developed to mitigate any possible faults arising from the sensor failures. Effectiveness of the proposed fault-tolerant estimation scheme is demonstrated through numerical analysis and CarSim simulation.

This paper presents a novel Kalman filter based road grade estimation method using measurements from an accelerometer, a gyroscope and a velocity sensor. The accelerometer measures the longitudinal proper acceleration of the vehicle, and the accelerometer measurement is almost drift free but it is heavily corrupted by the accelerometer noise. The gyroscope measures the pitch rate of the vehicle, and the gyroscope measurement is quite clean but it is substantially disturbed by the gyroscope bias. The velocity sensor measures the longitudinal velocity of the vehicle, and the velocity sensor measurement is also considerably corrupted by the measurement noise. The developed Kalman filter based estimation method uses the models of the sensors and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The simulation results show that the developed method is very effective in producing an accurate road grade estimate.

Micro-machined planar orifice nozzles have been developed and used with commercially produced diesel injection systems. Such a system may have the capability to improve the spray characteristics in DI diesel engines. The availability of a MEMS (Micro-Electro-Mechanical-Systems) processing sequence supported the construction of micro-planar orifice nozzles, and micro-systems technology was also employed in our macro-instrumentation. To demonstrate this process, fourteen MEMS nozzles were fabricated with deep X-ray lithography and electroplating technology. The circular orifice diameters were varied from 40 to 260 microns and the number of orifices varied from one to 169. Three plates with non-circular orifices were also fabricated to examine the effect of orifice shape on spray characteristics. These nozzles were then attached to commercial injectors and the associated injection systems were used for the spray experiments.

Remote vehicle start systems are commonly available as an aftermarket accessory, and more recently, as a factory installed vehicle feature. These systems and their associated algorithms enable a user of the vehicle to remotely start the engine and/or other vehicle systems with the end goal of preconditioning the cabin environment, for example, if the user wishes to have the vehicle's interior heated or cooled before the user enters the vehicle. However, if the engine is remotely started for an extended period of time, the increased use of fuel, energy, and/or other resources may be greater than optimal or desired. Through the use of available vehicle sensors and enhanced algorithms, a system can be implemented which allows the passenger cabin to be heated or cooled to within a range of moderate temperatures, while reducing the resources utilized by the vehicle.

An accurate estimation of cycle-by-cycle in-cylinder mass and the composition of the cylinder charge is required for spark-ignition engine transient control strategies to obtain required torque, Air-Fuel-Ratio (AFR) and meet engine pollution regulations. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) sensors have been utilized in different control strategies to achieve these targets; however, these sensors have response delay in transients. As an alternative to air flow metering, in-cylinder pressure sensors can be utilized to directly measure cylinder pressure, based on which, the amount of air charge can be estimated without the requirement to model the dynamics of the manifold.

In recent years, there is increasing demand for every CAE engineer on their confidence level of the virtual simulation results due to the upfront robust design requirement during early stage of an automotive product development. Apart from vehicle feel factor NVH characteristics, there are certain vibration target requirements at system or component level which need to be addressed during design stage itself in order to achieve the desired functioning during vehicle operating conditions. Vehicle passive safety system is one which primarily consists of acceleration sensors, control module and air-bag deployment system. Control module’s decision is based on accelerometer sensor signals so that its mounting locations should meet the sufficient inertance or dynamic stiffness performance in order to avoid distortion in signals due to its structural resonances.

The engineering of electric propulsion systems requires time and cost efficient methodologies to determine system characteristics as well as potential component integration issues. A significant part of this analysis is the identification of the electromagnetic fields present in the propulsion system. Understanding of the electromagnetic fields during system operation is a significant design consideration due to the use of components that require large current(s) and high voltage(s) in the proximity of other control system items (such as sensors) that operate with low current(s) and voltage(s). Therefore, it is critical to quantify the electromagnetic fields produced by these components within the design and how they may interact with other system components. Often overlooked (and also extremely important) is an evaluation of how the overall system architecture can generate or react to electromagnetic fields (which may be a direct result of packaging approaches).

Recent trends in the automotive industry show growing demands for the introduction of new in-vehicle features (e.g., smart-phone integration, adaptive cruise control, etc.) at increasing rates and with reduced time-to-market. New technological developments (e.g., in-vehicle Ethernet, multi-core technologies, AUTOSAR standardized software architectures, smart video and radar sensors, etc.) provide opportunities as well as challenges to automotive designers for introducing and implementing new features at lower costs, and with increased safety and security. As a result, the design of Electrical/Electronic (E/E) architectures is becoming increasingly challenging as several hardware resources are needed. In our earlier work, we have provided top-level definitions for three relevant metrics that can be used to evaluate E/E architecture alternatives in the early stages of the design process: flexibility, scalability and expandability.

Automotive HW/SW architectures are becoming increasingly complex to support the deployment of new safety, comfort, and energy-efficiency features. Such architectures include several software tasks (100+), messages (1000+), computational and communication resources (70+ CPUs, 10+ buses), and (smart) sensors and actuators (20+). To cope with the increasing system complexity at lowest development and product costs, highest safety, and fastest time to market, model-based rapid-prototyping development processes are essential. The processes, coupled with optimization steps aimed at reducing the number of software and hardware resources while satisfying the safety requirements, enable reduction of the system complexity and ease downstream testing/validation efforts. This paper describes a novel model-based design exploration and optimization process for the deployment of a set of software tasks on a dual-processor control module implementing a fail-safe strategy.

This paper presents advanced and cost-reducing technologies of a motor drive system with reduced permanent magnets but without a position sensor. The key enabler is the integration of novel designs of flux-intensifying interior permanent magnet synchronous machines (FI-IPMSMs) and position self-sensing control technologies. In this paper, we focus on two advantages of FI-IPMSM over conventional flux-weakening interior permanent magnet synchronous machines (FW-IPMSMs). The first benefit is that thinner magnets are possible and there is less concern for demagnetization because of its significantly smaller flux-weakening current. This paper presents two design examples of FI-IPMSMs, one of which has not only smaller magnets but also similar power conversion capability. The second advantage is reduced saturation and cross-saturation effect, which leads to improved position self-sensing capability.

Automobile drivers/passengers perceive automatic transmission (AT) shift quality through the torque transferred by the transmission. Clearly, torque regulation is important for transmission control. Unfortunately, a physical torque sensor has been too costly for production applications. With no torque measurement for feedback, controls in AT is mainly implemented in an open-loop fashion. Therefore, complicated adaptation algorithms are necessary while undesired shifts may still occur. To further simplify the controls and enhance its consistency and robustness, a direct torque feedback has long been desired in transmission control synthesis and development. A “virtual” torque sensor (VTS) algorithm has recently been developed to show a good potential in estimating relative torque along transmission output shaft using transmission output speed sensor and wheel speed sensors.

Diesel particle filters (DPF) have become the standard and essential aftertreatment components for all on-road diesel engines used in the US and Europe. The OBD requirements for DPF are becoming rigorously strict starting from 2015 model year. The pressure sensor or other strategies currently used for DPF diagnostics will most likely become insufficient to meet the new OBD requirements and a post DPF soot sensor might be necessary. This means that it will be even more imperative to develop a DPF design that would not have any soot leaks in its emission lifetime, otherwise the DPF will become a high warranty item.

A new press/die system for restraining force control has been developed in order to facilitate an increased level of process control in sheet metal forming. The press features a built-in system for controlling drawbead penetration in real time. The die has local force transducers built into the draw radius of the lower tooling. These sensors are designed to give process information useful for the drawbead control. This paper focuses on developing models of the drawbead actuators and the die shoulder sensors. The actuator model is useful for developing optimal control methods. The sensor characterization is necessary in order to develop a relationship between the raw sensor outputs and a definitive process characteristic such as drawbead restraining force (DBRF). Closed loop control of local specific punch force is demonstrated using the die shoulder sensor and a PID controller developed off-line with the actuator model.

The effect of wall temperature on single surface flame quenching and flame structure of an atmospheric premixed methane-air flame was studied. The luminous region of a laminar flame was located at an angle of 45 degrees to a temperature- controlled surface. C2 laser-induced fluorescence was used as an indicator of flame position while Raman spectroscopy was used to determine gas temperature profiles near the surface. These measurements were conducted for wall positions (vertical distance from the surface) ranging from 50 mm to 1.0 mm and wall temperatures ranging from 150 °C to 600 °C. C2 laser-induced fluorescence measurements indicated flame position is affected by the presence of a surface and the surface temperature. Larger C2 fluorescence intensity values were observed for higher wall temperatures at all distances from the surface.

The SAE J2716 SENT (Single Edge Nibble Transmission) Protocol has entered production with a number of announced products. The SENT protocol is a point-to-point scheme for transmitting signal values from a sensor to a controller. It is intended to allow for high resolution data transmission with a lower system cost than available serial data solution. The SAE SENT Task Force has developed a number of enhancements and clarifications to the original specification which are summarized in this paper.

During input tracking, closed-loop performance is strongly influenced by the dynamic of the system under control. Internal and external delays, such as actuation and measurement delays, have a detrimental effect on the bandwidth and stability. Additionally, production controllers are discrete in nature and the sampling time selection is another critical factor to be considered. In this paper we analyze the impact of both transported delay and controller sampling time on tracking performance using an electric machine speed-control problem as an example. A simple linear PI controller is used for this exercise. Furthermore, we show how the PI parameters can be adjusted to maintain a certain level of performance as the delays and sampling times are modified. This is achieved through an optimization algorithm that minimizes a specifically designed cost function.