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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.

It is well known that hydrocarbon reduction during a cold start is a major issue in achieving ultra low emissions standards. This paper describes one of the possible approaches for reducing the cold-start hydrocarbon emissions by using a fast “light-off” planar oxygen sensor. The goal of this study was to verify the operation characteristics of Delphi's fast “light-off” planar oxygen sensor's (INTELLEK OSP) operating characteristics and the closed-loop performance for achieving improved hydrocarbon control for stringent emission standards. Tests were conducted in open-loop and closed-loop mode under steady and transient conditions using a 1996 model year 2.4-liter DOHC in-line 4-cylinder engine with a close-coupled catalytic converter. Overall performance of the OSP showed relatively quick reaction time to reach the operating temperature.

Both evaporative emissions and tailpipe emissions have been reduced by more than 90% from uncontrolled levels in state-of-the-art. However, now that the objective is to reach near-zero emission levels, the need for aggressive purging of the canister and fuel tank and the need for extremely precise control of engine Air/Fuel ratio (A/F) come into conflict. On-board diagnostics and the wide variation in operating conditions and fuel properties in the “real world” add to the challenge of resolving these conflicting requirements. An advanced canister purge algorithm has been developed which substantially eliminates the effect of canister purge on A/F control by estimating and compensating for the fuel and air introduced by the purge system. This paper describes the objectives and function of this algorithm and the validation of its performance.

A Barometric Pressure Estimator (BPE) algorithm was implemented in a production speed-density Engine Management System (EMS). The BPE is a model-based, easily calibrated algorithm for estimating barometric pressure using a standard set of production sensors, thereby avoiding the need for a barometric pressure sensor. An accurate barometric pressure value is necessary for a variety of engine control functions. By starting with the physics describing the flow through the induction system, an algorithm was developed which is simple to understand and implement. When used in conjunction with the Pneumatic and Thermal State Estimator (PSE and TSE) algorithms [2], the BPE requires only a single additional calibration table, generated with an automated processing routine, directly from measured engine data collected at an arbitrary elevation, in-vehicle or on a dynamometer. The algorithm has been implemented on several different engines.

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.

Advances in sensor solutions in the automotive sector make it possible to develop better ADAS and autonomous driving functions. One of the main tasks of highway chauffeur and highway pilot automated driving systems is to keep the vehicle between the lane lines while driving on a pre-defined route. This task can be achieved by using camera and/or GPS to localize the vehicle between the lane lines. However, both sensors have shortcomings in certain scenarios. While the camera does not work when there are no lane lines to be detected, an RTK GPS can localize the vehicle accurately. On the other hand, GPS requires at least 3 satellite connections to be able to localize the vehicle and more satellite connections and real-time over-the-air corrections for lane-level positioning accuracy. If GPS localization fails or is not accurate enough, lane line information from the camera can be used as a backup.

A method of canceling unknown angular rate effects in impact immunity measurement for angular rate sensors is presented. A pair of the same type of testing sensors is arranged such that the sensing axes of the sensor pair are 180° out of phase. While an angular rate produces anti-phase component in the sensor outputs, a linear acceleration produces in-phase response from the sensors due to similar mechanical symmetry. This phase difference is used to cancel the angular rate component even though the actual angular rate may still be unknown. This cancellation can be derived from the sensor output transfer function and is supported with our experimental data.

Since the late 1980s, Delphi Automotive Systems has been very involved with the practical development of a variety of Collision Avoidance products for the near- and long-term automotive market. Many of these complex collision avoidance products will require the integration of various vehicular components/systems in order to provide a cohesive functioning product that is seamlessly integrated into the vehicle infrastructure. One such example of this system integration process was the development of an Adaptive Cruise Control system on an Opel Vectra. The design approach heavily incorporated system engineering processes/procedures. The critical issues and other technical challenges in developing these systems will be explored. Details on the hardware and algorithms developed for this vehicle, as well as the greater systems integration issues that arose during its development will also be presented.

Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically.

A control oriented model of a Dual Clutch Transmission was developed for real time Hardware In the Loop (HIL) applications. The model is an innovative attempt to reproduce the fast dynamics of the actuation system maintaining a step size large enough for real time applications. The model comprehends a detailed physical description of hydraulic circuit, clutches, synchronizers and gears, and simplified vehicle and internal combustion engine sub-models; a stable real time simulation is achieved with a simplification of the model without losing physical validity. After an offline validation, the model was implemented in a HIL system and connected to the TCU (Transmission Control Unit) via two input-output boards, and to a load plate which comprehends all the actuators.

Delphi is developing a new combustion technology called Gasoline Direct-injection Compression Ignition (GDCI), which has shown promise for substantially improving fuel economy. This new technology is able to reuse some of the controls common to traditional spark ignition (SI) engines; however, it also requires several new sensors and actuators, some of which are not common to traditional SI engines. Since this is new technology development, the required hardware set has continued to evolve over the course of the project. In order to support this development work, a highly capable and flexible electronic control system is necessary. Integrating all of the necessary functions into a single controller, or two, would require significant up-front controller hardware development, and would limit the adaptability of the electronic controls to the evolving requirements for GDCI.

In recent years, the desire for improved vehicle performance, reliability and safety have increased the electrical content and its complexity in vehicles. Advanced automotive systems integrate sensors, controllers, actuators and communication networks. To maintain safety and reliability, a comprehensive system of diagnostics and physical and analytic redundancy are used. In some cases, diagnostic strategies based on analytical redundancy can provide detection, as well as fault-tolerance, and may provide benefits in cost, packaging, flexibility and reusability. This paper discusses a range of diagnostic methods and their applicability to advanced automotive systems such as X-by-Wire. It will also show the reduction to practice of an advanced analytical technique for an automotive application.

An important part of automotive driving assistance systems and autonomous vehicles is speed optimization and traffic flow adaptation. Vehicle sensors and wireless communication with surrounding vehicles and road infrastructure allow for predictive control strategies taking near-future road and traffic information into consideration to improve fuel economy. For the development of autonomous vehicle speed control algorithms, it is imperative that the controller can be evaluated under different realistic driving and traffic conditions. Evaluation in real-life traffic situations is difficult and experimental methods are necessary where similar driving conditions can be reproduced to compare different control strategies. A traditional approach for evaluating vehicle performance, for example fuel consumption, is to use predefined driving cycles including a speed profile the vehicle should follow.

This paper describes a semi-active damping control system that responds in real-time to road and driving conditions based on body motions as determined through ABS wheel speed sensors. The use of these existing sensors for vehicle information eliminates the need for the additional sensors (e.g. accelerometers and body-to-wheel position/velocity sensors) that are commonly part of semi-active suspension systems. This technology also allows for further cost and part count reductions through the combination of the suspension and brake controls into a single electronic control unit. This paper has been previously presented in 1998 at the SAE Controlled Suspension System Toptec.

An algorithm for estimation of vehicle yaw rate and side slip angle using steering wheel angle, wheel speed, and lateral acceleration sensors is proposed. It is intended for application in vehicle stability enhancement systems, which use controlled brakes or steering. The algorithm first generates two initial estimates of yaw rate from wheel speeds and from lateral acceleration. A new estimate is subsequently calculated as a weighted average of the two initial ones, with the weights proportional to confidence levels in each estimate. This preliminary estimate is fed into a closed loop nonlinear observer, which generates the final estimate of yaw rate along with estimates of lateral velocity and side slip angle. Parameters of the observer depend on the estimated surface coefficient of adhesion, thus providing adaptation to changes in road surface coefficient of adhesion.

RACam [1] is an Active Safety product designed and manufactured at Delphi and is part of their ADAS portfolio. It combines two sensors - Electronically Scanned RADAR and Camera in a single package. RADAR and Vision fusion data is used to realize safety critical systems such as Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Lane Departure Warning (LDW), Lane Keep Assist (LKA), Traffic Sign Recognition (TSR) and Automatic Headlight Control (AHL). Figure 1 RACam Front View. With an increase in Active Safety features in the automotive market there is also a corresponding increase in the complexity of the hardware which supports these safety features. Delphi’s hardware design for Active Safety has evolved over the years. In Delphi’s RACam product there are a number of critical components required in order to realize RADAR and Vision in a single package. RACam is also equipped with a fan and heater to improve the operating temperature range.

The maturity reached in the development of Unmanned Air Vehicles (UAVs) systems is making them more and more attractive for a vast number of civil missions. Clearly, the introduction of UAVs in the civil airspace requiring practical and effective regulation is one of the most critical issues being currently discussed. As several civil air authorities report in their regulations “Sense and Avoid” or “Detect and Avoid” capabilities are critical to the successful integration of UAV into the civil airspace. One possible approach to achieve this capability, specifically for operations beyond the Line-of-Sight, would be to equip air vehicles with a vision-based system using cameras to monitor the surrounding air space and to classify other air vehicles flying in close proximity. This paper presents an image-based application for the supervised classification of air vehicles.

In this paper we discuss in detail an algorithm that addresses cylinder-to-cylinder imbalance issues. Maintaining even equivalence-ratio (ϕ) control across all the cylinders of an engine is confounded by imbalances which include fuel-injector flow variations, fresh-air intake maldistribution and uneven distribution of Exhaust Gas Re-circulation (EGR). Moreover, in markets that are growing increasingly cost conscious, with ever tightening emissions regulations, correcting for such mismatches must not only be done, but done at little or no additional cost. To address this challenge, we developed an Individual Cylinder Fuel Control (ICFC) algorithm that estimates each cylinder's individual ϕ and then compensates to correct for any imbalance using only existing production hardware. Prior work in this area exists1,2, yet all disclosed production-intent work was performed using wide-range oxygen sensors, representing cost increases.

The purpose of this paper is to present some innovative techniques developed for an unconventional utilization of currently standard exhaust sensors, such as HEGO, UEGO, and NOx probes. In order to comply with always more stringent legislation about pollutant emissions, intake-exhaust systems are becoming even more complex and sophisticated, especially for CI engines, often including one or two UEGO sensors and a NOx sensor, and potentially equipped with both short-route and long-route EGR. Within this context, the effort to carry out novel methods for measuring the main exhaust gas dynamic properties exploiting sensors installed for different purposes, could be useful both for control applications, such as EGR rates estimation, or cost reduction, minimizing the on-board devices number. In this work, a gray-box model for measuring the gas mass flow rate, based on standard NOx sensor operating parameters of its heating circuit, is analyzed.

The increasing features and complexity of today's automotive architectures are becoming increasingly difficult to manage. Each new innovation typically requires additional mechanical actuators and associated electrical controllers. The sheer number of black boxes and wiring are being limited not by features or cost but by the inability to physically assemble them into a vehicle. A new architecture is required which will support the ability to add new features but also enable the Vehicle Assembly Plants to easily assemble and test each subsystem. One such architecture is a distributed multiplex arrangement that reduces the number of wires while enabling flexibility and expandability. Previous versions have had to deal with issues such as noise immunity at high switching currents. The LIN Bus with its low cost and rail-to-rail capability may be the key enabling technology to make the multiplexed architecture a reality.