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Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure.

The automotive industry has been witnessing a major shift towards downsized boosted direct injection engines due to diminishing petroleum reserves and increasingly stringent emission targets. Boosted engines operate at a high mean effective pressure (MEP), resulting in higher in-cylinder pressures and temperatures, effectively leading to increased possibility of abnormal combustion events like knock and pre-ignition. Therefore, the compression ratio and boost pressure in modern engines are restricted, which in-turn limits the engine efficiency and power. To mitigate conditions where the engine is prone to knocking, the engine control system uses spark retard and/or mixture enrichment, which decrease indicated work and increase specific fuel consumption. Several researchers have advocated water injection as an approach to replace or supplement existing knock mitigation techniques.

Traditional EGR measurement systems using delta pressure over a fixed orifice such as a DPFE sensor (Delta Pressure Feedback for EGR), have limitations in the ability to measure EGR accurately. Also, the pressure drop that results from the orifice may not be acceptable in some applications. To measure the EGR accurately and without any pressure loss, a new measurement system was developed that uses an oxygen sensor in the intake air. In this paper, the technology of using an oxygen sensor to measure the EGR concentration is discussed. The paper details the EGR measurement principle with an oxygen sensor and the associated mathematical relations of translating the oxygen measurement to EGR measurement. Factors affecting the EGR measurement such as the air/fuel ratio of the EGR, intake air pressure, and diffusion effects of the EGR constituents are discussed in detail. Compensation mechanisms are explained and associated results shown.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

Lean NOx Traps are used to treat the NOx emissions from lean-burn engines by storing the NOx under lean conditions and reducing the NOx during periodic rich excursions. However, sulfur poisons the adsorption sites of the traps. The sulfur can be removed from the NOx trap by operating rich at high temperatures for several minutes. This results in the release of some SO2 but also large quantities of H2S, which is a source of customer dissatisfaction that must be reduced or eliminated. This paper describes the use of a nickel-containing catalyst and air/fuel control to maximize the release of SO2 and minimize the emissions of H2S during the desulfation of a lean NOx trap. We present laboratory and vehicle data with a nickel-containing catalyst located downstream of a lean NOx trap during desulfations of the trap. The nickel effectively reduced the emissions of H2S during the desulfation while improving the robustness to fluctuations in the air/fuel control.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.

Reduced engine idle speed reduces fuel consumption but requires active idle speed control (ISC) to avoid stalls due to accessory load disturbances. For gasoline engines, spark advance is used in conjunction with air flow for the idle speed control. However, for spark control to be effective the nominal spark timing has to be retarded from the optimal timing to allow spark to increase torque. This offsets the fuel consumption benefit from lower speeds. During lean homogenous operating modes, Fuel Assisted ISC (FA-ISC) uses fuel to increase torque (similar to diesel and gasoline stratified charge) eliminating the need for the retarded nominal spark. The engine then operates close to optimal spark and the lean air fuel limit for optimal fuel economy.

Engine Mapping is usually performed under nominal conditions which include a humidity level of 8 g/Kg. Customers driving at different humidity conditions (which may range from 1 g/Kg in dry and colder climates and up to 35 g/Kg as in tropical climates) may experience a degraded performance due to the errors in engine torque estimation provided by the ECU. The torque estimation error interacts with many other features that affect drivability, such as the peak performance of the engine, transmission shift quality, etc. This paper extends the investigation in Part-1 by analyzing and quantifying the torque estimation error that may result in certain customer use cases at high humidity conditions, due to the mismatch between calibrated and actual conditions. The analysis is mainly performed for Speed-Density systems (MAP sensor based) but the effect of mass air flow sensor (MAF sensor) based systems is also briefly considered.

Engine Mapping is usually performed under nominal conditions which include a humidity level of 8 g/Kg. Customers driving at different conditions (which may range from 1 g/Kg in colder and dry climates and up to 35 g/Kg as in tropical climates) may experience less-than-optimal engine combustion which results in reduced onroad fuel economy. Humidity has an EGR-equivalent effect, and measuring it will correct the spark timing, mainly at Maximum Brake Torque (MBT) and borderline conditions, and claim back some of those losses. This paper aims at quantifying the small fuel economy benefits associated with on-board humidity measurement for certain customer use cases at high humidity conditions. Dyno data was collected for a Ford 2.3L GTDI engine at three speed load points, and intake air humidity was varied between 20% and 80% relative humidity. The effect of humidity compensation on spark timing, combustion phasing, knock, and consequently on overall engine efficiency was analyzed.

Low Pressure (LP) Exhaust Gas Recirculation (EGR) promises fuel economy benefits at high loads in turbocharged SI engines as it allows better combustion phasing and reduces the need for fuel enrichment. Precise estimation and control of in-cylinder EGR concentration is crucial to avoiding misfire. Unfortunately, EGR flow rate estimation using an orifice model based on the EGR valve ΔP measurement can be challenging given pressure pulsations, flow reversal and the inherently low pressure differentials across the EGR valve. Using a GT-Power model of a 1.6 L GDI turbocharged engine with LP-EGR, this study investigates the effects of the ΔP sensor gauge-line lengths and measurement noise on LP-EGR estimation accuracy. Gauge-lines can be necessary to protect the ΔP sensor from high exhaust temperatures, but unfortunately can produce acoustic resonance and distort the ΔP signal measured by the sensor.

The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.

Dual fuel injection systems, like PFI+DI (port fuel injection + direct injection system) are being increasingly used in gasoline engine applications to increase the engine performance, fuel efficiency and reduce emissions. At a given engine operating condition, the air/fuel error is a function of the fraction of fuel injected by each of the fuel systems. If the fraction of fuel from each of the fuel system is changed at a given operating condition, the fuel system error will change as well making it challenging to learn the fuel system errors. This paper aims at describing the adaptive fueling control algorithm to estimate the fuel error contribution from each individual fuel system. Considering the fuel injection system slope errors to be the significant cause for air-fuel errors, a model structure was developed to calculate the fuel system adaptive correction factor as a function of changing fraction of fueling between the fuel systems.