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In the face of growing concerns about environmental sustainability and urban congestion, the integration of eco-driving strategies has emerged as a pivotal solution in the field of the urban transportation sector. This study explores the potential benefits of a CAV functioning as a virtual eco-driving controller in an urban traffic scenario with a group of following human-driven vehicles. A computationally inexpensive and realistic powertrain model and energy management system of the Chrysler Pacifica PHEV are developed with the field experiment data and integrated into a forward-looking vehicle simulator to implement and validate an eco-driving speed planning and energy management strategy assuming longitudinal automation. The eco-driving algorithm determines the optimal vehicle speed profile and energy management strategy.

Accurately predicting the future behavior of the surrounding traffic, especially the velocity of the lead vehicle is important for optimizing the energy consumption and improve the safety of Connected and Automated Vehicles (CAVs). Several studies report methods to predict short-to-mid-length lead vehicle velocity using stochastic models or other data-driven techniques, which require availability of extensive data and/or Vehicle-to-Vehicle (V2V) communication. In the absence of connectivity, or in data-restricted cases, the prediction must rely only on the measured position and relative velocity of the lead vehicle at the current time. This paper proposes two velocity predictors to predict short-to-mid-length lead vehicle velocity. The first predictor is based on a Constant Acceleration (CA) with an augmented stop mode. The second one is based on a modified Enhanced Driver Model (EDM-LOS) with line-of-sight feature.

This research leverages publicly available crash data to construct safety-critical scenarios focusing primarily on Level 3 Automated Driving Systems (ADS) safety assessment under highway driving conditions. NHTSA’s Crashworthiness Data System (CDS) has a rich dataset of representative crashes sampled from numerous Primary Sampling Units (PSUs) across the country. Each of these datasets includes the storyline, road geometry information, detailed description of actors involved in the crash, weather information, scene diagrams, crash images, and a myriad of other crash-specific details. The methodology adopted aims to generate critical scenarios from real-world driving to complement the existent regulatory tests for the validation of L3 ADS. For this work, a four-step approach was adopted to extract safety-critical scenarios from crash data.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

A quasi-dimensional model for a direct injection diesel engine was developed based on experiments at Sandia National Laboratory. The Sandia researchers obtained images describing diesel spray evolution, spray mixing, premixed combustion, mixing controlled combustion, soot formation, and NOx formation. Dec [1] combined all of the available images to develop a conceptual diesel combustion model to describe diesel combustion from the start of injection up to the quasi-steady form of the jet. The end of injection behavior was left undescribed in this conceptual model because no clear image was available due to the chaotic behavior of diesel combustion. A conceptual end-of-injection diesel combustion behavior model was developed to capture diesel combustion throughout its life span. The compression, expansion, and gas exchange stages are modeled via zero-dimensional single zone calculations.

The Ohio State University EcoCAR 3 team is building a plug-in hybrid electric vehicle (PHEV) post-transmission parallel 2016 Chevrolet Camaro. With the end-goal of improving fuel economy and reducing tail pipe emissions, the Ohio State Camaro has been fitted with a 32 kW alternator-starter belt coupled to a 119 kW 2.0L GDI I4 engine that runs on 85% ethanol (E85). The belted alternator starter (BAS) which aids engine start-stop operation, series mode and torque assist, is powered by an 18.9 kWh Lithium Iron Phosphate energy storage system, and controlled by a DC-AC inverter/controller. This report details the modeling, calibration, testing and validation work done by the Ohio State team to fast track development of the BAS system in Year 2 of the competition.

Global fuel economy and CO2 reduction mandates are driving the need for a substantial increase in vehicle fuel efficiency over the next several years, with improvements coming from many sources. This paper describes a vehicle demonstration program to improve fuel economy by implementing a systems approach to reduce friction and parasitic losses. The work concentrated on nearer term technologies that can be quickly transferred to production vehicle programs. Major technologies demonstrated on the vehicle included gasoline direct injection (GDi) with cooled EGR, advanced valvetrain, rollerization of both crankshaft and camshaft, and stop-start engine operation. The work described in this paper comprises Phase I of a two phase program funded by DOE contract DE-EE0003258. It includes a hardware overview and a description of the system development activities. Results focus on vehicle fuel economy benefits compared to a production baseline vehicle.

The Rotating Liner Engine (RLE) is an engine design concept where the cylinder liner rotates in order to reduce piston assembly friction and liner/ring wear. The reduction is achieved by the elimination of the mixed and boundary lubrication regimes that occur near TDC. Prior engines for aircraft developed during WW2 with partly rotating liners (Sleeve Valve Engines or SVE) have exhibited reduction of bore wear by factor of 10 for high BMEP operation, which supports the elimination of mixed lubrication near the TDC area via liner rotation. Our prior research on rotating liner engines experimentally proved that the boundary/mixed components near TDC are indeed eliminated, and a high friction reduction was quantified compared to a baseline engine. The added friction required to rotate the liner is hydrodynamic via a modest sliding speed, and is thus much smaller than the mixed and boundary friction that is eliminated.

To perform coastdown tests on heavy-duty trucks, both long acceleration and coasting distances are required. It is very difficult to find long flat stretches of road to conduct these tests; for a Class 8 truck loaded to 80,000 lb, about 7 miles of road is needed to complete the coastdown tests. In the present study, a method for obtaining coastdown coefficients from data taken on a road of variable grade is presented. To this end, a computer code was written to provide a fast solution for the coastdown coefficients. Class 7 and Class 8 trucks were tested with three different weight configurations: empty, “cubed-out” (fully loaded but with a payload of moderate density), and “weighed-out” (loaded to the maximum permissible weight).

EmiSense Technologies, LLC (www.emisense.com) is commercializing its electronic particulate matter (PM) sensor that is based on technology developed at the University of Texas at Austin (UT). To demonstrate the capability of this sensor for real-time PM measurements and on board diagnostics (OBD) for failure detection of diesel particle filters (DPF), independent measurements were performed to characterize the engine PM emissions and to compare with the PM sensor response. Computational fluid dynamics (CFD) modeling was performed to characterize the hydrodynamics of the sensor's housing and to develop an improved PM sensor housing with reproducible hydrodynamics and an internal baffle to minimize orientation effects. PM sensors with the improved housing were evaluated in the truck exhaust of a heavy duty (HD) diesel engine tested on-road and on a chassis dynamometer at the University of California, Riverside (UCR) using their Mobile Emissions Laboratory (MEL).

Gasoline direct injection (GDi) engines have become popular due to their inherent potential for reduction of exhaust emissions and fuel consumption to meet increasingly stringent environmental standards. These engines require high-pressure fuel injection in order to improve the fuel atomization process and accelerate mixture preparation. The injector is a critical part of this system. The injector technology needed to satisfy the market demands is constantly changing. This paper focuses on how the Design for Six Sigma innovation methodology was successfully used to develop a new injector for the homogeneous direct injection market. The project begins with the work to understand the market needs and market drivers then decomposes those needs into functional requirements and concepts. The concepts are evaluated and the best concept is selected. The project ends with the optimization of the critical functions including fuel flow control and fuel spray control.

This paper describes a correlation study on fuel spray pattern recognition of multi-hole injectors for gasoline direct injection (GDi) engines. Spray pattern is characterized by patternation length, which represents the distance of maximum droplet concentration from the axis of the injector. Five fuel injectors with different numbers and sizes of nozzle holes were considered in this study. Experimental data and CFD modeling results were used separately to develop regression models for spray patternation. These regressions predicted the influence of a number of injector operating and design parameters, including injection system operating pressure, valve lift, injector hole length-to-diameter ratio (L/d) and the orientation of the injector hole. The regression correlations provided a good fit with both experimental and CFD spray simulation results. Thus CFD offers a good complement to experimental validation during development efforts to meet a desired injector spray pattern.

An electronic particulate matter sensor (EPMS) developed at the University of Texas was used to characterize exhaust gases from a single-cylinder diesel engine and a light-duty diesel vehicle. Measurements were made during transient tip-in events with multiple sensor configurations in the single-cylinder engine. The sensor was operated in two modes: one with the electric field energized, and the other with no electric field present. In each mode, different characteristic signals were produced in response to a tip-in event, highlighting the two primary mechanisms of sensor operation. The sensor responded to both the natural charge of the particulate matter (PM) emitted from the engine, and was also found to create a signal by charging neutral particles. The characteristics of the two mechanisms of operation are discussed as well as their implications on the placement and operation of the sensor.

Trucks operating in inter-modal (drayage) operation in and around port and rail terminals, are responsible for a large proportion of the emissions of NOX, which are problematic for the air quality of the Houston and Dallas/Ft. Worth metro areas. A standard test cycle, called the Texas Dray Truck Cycle, was developed to represent the operation of heavy-duty diesel trucks in dray operations. The test cycle reflects the substantial time spent at idle (~45%) and the high intensity of the on-road portions. This test cycle was then used in the SAE J1321 test protocol to evaluate the effect on fuel consumption and NOX emissions of retrofitting dray trucks with light-weight, low-rolling resistance wide-single tires. In on-track testing, a reduction in fuel consumption of 8.7% was seen, and NOX emissions were reduced by 3.8% with the wide single tires compared to the conventional tires.

This paper presents the latest developments in the design and performance of an electronic particulate matter (PM) sensor developed at The University of Texas at Austin (UT) and suitable, with further development, for applications in active engine control of PM emissions. The sensor detects the carbonaceous mass component of PM in the exhaust and has a time-resolution less than 20 (ms), allowing PM levels to be quantified for engine transients. Sample measurements made with the sensor in the exhaust of a single-cylinder light duty diesel engine are presented for both steady-state and transient operations: a steady-state correlation with gravimetric filter measurements is presented, and the sensor response to rapid increases in PM emission during engine transients is shown for several different tip-in (momentary increases in fuel delivery) conditions.

The basic process of spark ignition in engines has changed little over the more than 100 years since its first application. The rapid evolution of several advanced engine concepts and the refinement of existing engine designs, especially applications of power boost technology, have led to a renewed interest in advanced spark ignition concepts. The increasingly large rates of in-cylinder dilution via EGR and ultra-lean operation, combined with increases in boost pressures are placing new demands on spark ignition systems. The challenge is to achieve strong and consistent ignition of the in-cylinder mixture in every cycle, to meet performance and emissions goals while maintaining or improving the durability of ignitor. The application of railplug ignition to some of these engine systems is seen as a potential alternative to conventional spark ignition systems that may lead to improved ignition performance.

Combustion chamber pressure measurement in engines via a passage is an old technique that is still widely used in engine research. This paper presents improved passage designs for an off-set electrode spark plug designed to accept a pressure transducer. The spark plug studied was the Champion model 304-063A. Two acoustic models were developed to compute the resonance characteristics. The new designs have a resonance frequency in a range higher than the fundamental frequency expected from knock so that the signal can be lowpass filtered to remove the resonance and not interfere with pressure signal components associated with combustion phenomena. Engine experiments verified the spark plug resonance behavior. For the baseline engine operating condition approximately 50 of 100 cycles had visible passage resonance in the measured pressure traces, at an average frequency of 8.03 kHz.

The Rotating Linear Engine (RLE) derives improved fuel efficiency and decreased maintenance costs via a unique lubrication design, which decreases piston assembly friction and the associated wear for heavy-duty natural gas and diesel engines. The piston ring friction exhibited on current engines accounts for 1% of total US energy consumption. The RLE is expected to reduce this friction by 50-70%, an expectation supported by hot motoring and tear-down tests on the UT single cylinder RLE prototype. Current engines have stationary liners where the oil film thins near the ends of the stroke, resulting in metal-to-metal contact. This metal-to-metal contact is the major source of both engine friction and wear, especially at high load. The RLE maintains an oil film between the piston rings and liner throughout the piston stroke due to liner rotation. This assumption has also been confirmed by recent testing of the single cylinder RLE prototype.