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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.

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.

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.

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.

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.

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.