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In this work, a dynamically loaded hydrodynamic journal bearing test rig is developed and introduced. The rig is a novel design, using a hydraulic actuator with fast acting spool valves to apply load to a connecting rod. This force is transmitted through the connecting rod to the large end bearing which is mounted on a spinning shaft. The hydraulic actuator allows for fully variable control and can be used to apply either static load in compression or tension, or dynamic loading to simulate engine operation. A variable speed electric motor controls shaft speed and is synchronized to the hydraulic actuator to accurately simulate loading to represent all four engine strokes. A high precision torque meter enables direct measurements of friction torque, while shaft position is measured via a high precision encoder.

The general benefits of automation are well documented. Order of magnitude improvements are achievable in processing speeds, production rates, and efficiency. Other benefits include improved process consistency (inversely, reduced process variation), reduced waste and energy consumption, and risk reduction to operators. These benefits are especially true for the automation of the aerospace paint removal (or "depaint") processes. Southwest Research Institute® (SwRI®) developed and implemented two systems in the early 1990s for depainting full-body fighter aircraft at Robins Air Force Base (AFB) at Warner Robins, Georgia, and Hill AFB at Ogden, Utah. These systems have been in production use, almost continuously for approximately 20 years, for the depainting of the F-15 Eagle and the F-16 Falcon fighter aircraft, respectively.

Connected and automated driving technology is known to improve real-world vehicle efficiency by considering information about the vehicle’s environment such as traffic conditions, traffic lights or road grade. This study shows how the powertrain of a hybrid-electric vehicle realizes those efficiency benefits by developing methods to directly measure real-time transient power losses of the vehicle’s powertrain components through chassis-dynamometer testing. This study is a follow-on to SAE Technical Paper 2019-01-0116, Test Methodology to Quantify and Analyze Energy Consumption of Connected and Automated Vehicles [1], to understand the sources of efficiency gains resulting from connected and automated vehicle driving. A 2017 Toyota Prius Prime was instrumented to collect power measurements throughout its powertrain and driven over a specific driving schedule on a chassis dynamometer.

Horiba on-board diesel exhaust particulate sampler (OBS-PM) is a filter based partial flow particulate sampling system used for On-board diesel particulate matter (PM) measurement. It takes sample from either raw or diluted exhaust. It can run at constant dilution ratios or at variable dilution ratios with proportional control on the sample flow. The diluted exhaust moves through a pre-weighed 47 mm particulate filter and PM is collected on the filter. By weighing the loaded sample filter, PM emission from the engine or the vehicle can be determined. The performance of the OBS-PM meets most of requirements for a real-time partial flow sample system (PFSS) recommended by ISO 16183 [2]. The physical size and the power consumption of the instrument are minimized. It is powered with four 12 volts batteries, and can be installed on a vehicle for real-world PM emission evaluation.

SwRI has developed the DCO® ignition system, a unique continuous discharge system that allows for variable duration/energy events in SI engines. The system uses two coils connected by a diode and a multi-striking controller to generate a continuous current flow through the spark plug of variable duration. A previous publication demonstrated the ability of the DCO system to improve EGR tolerance using low energy coils. In this publication, the work is extended to high current (≻ 300 mA/high energy (≻ 200 mJ) coils and compared to several advanced ignition systems. The results from a 4-cylinder, MPI application demonstrate that the higher current/higher energy coils offer an improvement over the lower energy coils. The engine was tested at a variety of speed and load conditions operating at stoichiometric air-fuel ratios with gasoline and EGR dilution.

Vehicle to Everything (V2X) communication has enabled on-board access to information from other vehicles and infrastructure. This information, traditionally used for safety applications, is increasingly being used for improving vehicle fuel economy [1-5]. This work aims to demonstrate energy consumption reductions in heavy/medium duty vehicles using an eco-driving algorithm. The algorithm is enabled by V2X communication and uses data contained in Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) to generate an energy-efficient velocity trajectory for the vehicle to follow. An urban corridor was modeled in a microscopic traffic simulation package and was calibrated to match real-world traffic conditions. A nominal reduction of 7% in energy consumption and 6% in trip time was observed in simulations of eco-driving trucks.

Air conditioning and heating of heavy-duty truck cabs is an important contributor to engine efficiency, fuel economy and driver comfort. The air conditioner condenser coil and engine radiator typically share a common cooling fan, making it necessary to run the large engine cooling fan to provide condenser cooling. Engagement of the radiator cooling fan consumes a large amount of energy, further contributing to engine exhaust and noise emissions. Even under moderate temperature conditions, when the conventional engine-driven air conditioning compressor is not in use, the belt drive system adds a small speed-dependent parasitic load to the engine. Electrically driven air conditioning systems have the potential for lower energy consumption than their mechanical counterparts: Electrically driven air conditioning systems can reduce engine idle time by decoupling the air conditioner system from the engine cooling fan while offering near zero parasitic load when not in use.

Improved designs of extruded metal electrically heated catalysts (EHC) in combination with a traditional converter achieved the California ultra-low emission vehicle (ULEV) standard utilizing 50% less electrical energy than previous prototypes. This energy reduction is largely achieved by reducing the mass of the EHC. In addition to energy reduction, the battery voltage is reduced from 24 volts to 12 volts, and the power is reduced from 12 kilowatts to 3 kilowatts. Also discussed is the impact EHC mass, EHC catalytic activity, and no EHC preheating has on non-methane hydrocarbon emissions, energy requirements, and power requirements.

This paper describes installation and testing of electrified engine accessories and fuel cell auxiliary power units for a Class-8 tractor. A 2.4 kW fuel cell APU (Auxiliary Power Unit) has been added to supply a 42 V power supply for electrification of air conditioning and water pump systems. A 42/12 V dual alternator was used to replace the OEM alternator to provide safety back-up in case of fuel cell failure. A QNX Real Time Operating System-based (RTOS) Rapid Prototype Electronic Control System (RPECS™), developed by Southwest Research Institute (SwRI™), is used for supervisory control and coordination between accessories and engine. A Controller Area Network (CAN) interface, from the engine Electronic Control Unit (ECU), and the RS232 interface, from the fuel cell controllers, provide system data and control for RPECS. Custom wiring to the hydrogen, water pump, and air conditioning systems also provide data to RPECS. The water pump system controller is autonomous.

A series of road, engine, chassis dynamometer, and accessory power consumption tests was conducted in order to characterize the fuel economy of 1973 standard and intermediate size vehicles. Devices and systems which appeared to offer fuel economy benefits were evaluated by means of an analytical procedure. The study was limited to hardware which could be in production by the 1980 model year. The evaluation procedure was based on urban and steady speed operation, and the effects of compliance with future emission standards were included. Combinations of individual improvements were selected and applied to the same vehicle. The evaluation procedure was repeated, and fuel economy improvements of 30 to 70% were predicted by comparison with 1973 model year vehicles.

Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1-4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6], but the impact in an urban setting has not been studied yet. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated with real-world data in PTV Vissim traffic microsimulation software.

Eco-driving algorithms enabled by Vehicle to Everything (V2X) communications in Connected and Automated Vehicles (CAVs) can improve fuel economy by generating an energy-efficient velocity trajectory for vehicles to follow in real time. Southwest Research Institute (SwRI) demonstrated a 7% reduction in energy consumption for fully loaded class 8 trucks using SwRI’s eco-driving algorithms. However, the impact of these schemes on vehicle emissions is not well understood. This paper details the effort of using data from SwRI’s on-road vehicle tests to measure and evaluate how eco-driving could impact emissions. Two engine and aftertreatment configurations were evaluated: a production system that meets current NOX standards and a system with advanced aftertreatment and engine technologies designed to meet low NOX 2031+ emissions standards.

A detailed component performance, ratings, and cost study was conducted on series and parallel hybrid electric vehicle (HEV) configurations for several battery pack and main electric traction motor voltages while meeting stringent Partnership for a New Generation of Vehicles (PNGV) power delivery requirements. A computer simulation calculated maximum current and voltage for each component as well as power and fuel consumption. These values defined the peak power ratings for each HEV drive system's electric components: batteries, battery cables, boost converter, generator, rectifier, motor, and inverter. To identify a superior configuration or voltage level, life cycle costs were calculated based on the components required to execute simulated drive schedules. These life cycle costs include the initial manufacturing cost of components, fuel cost, and battery replacement cost over the vehicle life.

The use of cooled EGR in gasoline engines improves the fuel efficiency of the engine through a variety of mechanisms, including improving the charge properties (e.g. the ratio of specific heats), reducing knock and enabling higher compression ratio operation and, at part loads conditions in particular, reducing pumping work. One of the limiting factors on the level of improvement from cooled EGR is the ability of the ignition system to ignite a dilute mixture and maintain engine stability. Previous work from SwRI has shown that, by increasing the ignition duration and using a continuous discharge ignition system, an improved ignition system can substantially increase the EGR tolerance of an engine [1, 2]. This improvement comes at a cost, however, of increased ignition system energy requirements and a potential decrease in spark plug durability. This work examines the impact of engine operating parameters on the ignition energy requirements under high dilution operation.

A new generation of vehicle dynamics and powertrain control technologies are being developed to leverage information streams enabled via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity [1, 2, 3, 4, 5]. While algorithms that use these connected information streams to enable improvements in energy efficiency are being studied in detail, methodologies to quantify and analyze these improvements on a vehicle have not yet been explored fully. A procedure to test and accurately measure energy-consumption benefits of a connected and automated vehicle (CAV) is presented. The first part of the test methodology enables testing in a controlled environment. A traffic simulator is built to model traffic flow in Fort Worth, Texas with sufficient accuracy. The benefits of a traffic simulator are two-fold: (1) generation of repeatable traffic scenarios and (2) evaluation of the robustness of control algorithms by introducing disturbances.

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application.

Onboard sensing and external connectivity using Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) technologies allows a vehicle to "know" its future operating environment with some degree of certainty, greatly narrowing prior information gaps. The increased development of such connected and automated vehicle systems, currently used mostly for safety and driver convenience, presents new opportunities to improve the energy efficiency of individual vehicles [1, 2, 3, 4, 5]. Southwest Research Institute (SwRI) in collaboration with Toyota Motor North America and University of Michigan is currently working on improving energy consumption of a Toyota Prius Prime 2017 by 20%. This paper will provide an overview of the various algorithms that are being developed to achieve the energy consumption target. Custom tools such as a traffic simulator was built to model traffic flow in Fort Worth, Texas with sufficient accuracy.

Eco-Driving with connected and automated vehicles has shown potential to reduce energy consumption of an individual (i.e., ego) vehicle by up to 15%. In a project funded by ARPA-E, a team led by Southwest Research Institute demonstrated an 8-12% reduction in energy consumption on a 2017 Prius Prime. This was demonstrated in simulation as well as chassis dynamometer testing. The authors presented a simulation study that demonstrated corridor-level energy consumption improvements by about 15%. This study was performed by modeling a six-kilometer-long urban corridor in Columbus, Ohio for traffic simulations. Five powertrain models consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles were developed. The design of experiment consisted of sweeps for various levels of traffic, penetration of smart vehicles, penetration of technology, and powertrain electrification.

Vehicle-to-everything (V2X) communication, primarily designed for communication between vehicles and other entities for safety applications, is now being studied for its potential to improve vehicle energy efficiency. In previous work, a 20% reduction in energy consumption was demonstrated on a 2017 Prius Prime using V2X-enabled algorithms. A subsequent phase of the work is targeting an ambitious 30% reduction in energy consumption compared to a baseline. In this paper, we present the Eco-routing algorithm, which is key to achieving these savings. The algorithm identifies the most energy-efficient route between an Origin-Destination (O-D) pair by leveraging information accessible through commercially available Application Programming Interfaces (APIs). This algorithm is evaluated both virtually and experimentally through simulations and dynamometer tests, respectively, and is shown to reduce vehicle energy consumption by 10-15% compared to the baseline over real-world routes.