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Reactivity Controlled Compression Ignition (RCCI) natural gas/diesel dual-fuel combustion has been shown to achieve high thermal efficiency with low NOX and PM emissions, but has traditionally been limited to low to medium loads. High BMEP operation typically requires high substitution rates (i.e., >90% NG), which can lead to high cylinder pressure, pressure rise rates, knock, and combustion loss. In previous studies, compression ratio was decreased to achieve higher load operation, but thermal efficiency was sacrificed. For this study, a multi-cylinder heavy-duty engine that has been modified for dual-fuel operation (diesel direct-injection and natural gas (NG) fumigated into the intake stream) was used to explore RCCI and other dual-fuel combustion modes at high compression ratio, while maintaining stock lug curve capability (i.e., extending dual-fuel operation to high loads where conventional diesel combustion traditionally had to be used).

Southwest Research Institute (SwRI) has successfully demonstrated the cooled EGR concept via the High Efficiency Dilute Gasoline Engine (HEDGE) consortium. Dilution of intake charge provides three significant benefits - (1) Better Cycle Efficiency (2) Knock Resistance and (3) Lower NOx/PM Emissions. But EGR dilution also poses challenges in terms of combustion stability, condensation and power density. The Dedicated EGR (D-EGR) concept brings back some of the stability lost due to EGR dilution by introducing reformates such as CO and H2 into the intake charge. Control of air, EGR, fuel, and ignition remains a challenge to realizing the aforementioned benefits without sacrificing performance and drivability. This paper addresses the DEGR solution from a controls standpoint. SwRI has been developing a unified framework for controlling a generic combustion engine (gasoline, diesel, dual-fuel natural gas etc.).

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.

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.

This paper explores the possibility of on-board fuel classification for fuel property adaptive compression-ignition engine control system. The fuel classifier is designed to on-board classify the fuel that a diesel engine is running, including alternative and renewable fuels such as bio-diesel. Based on this classification, the key fuel properties are provided to the engine control system for optimal control of in-cylinder combustion and exhaust treatment system management with respect to the fuel. The fuel classifier employs engine input-output response characteristics measured from standard engine sensors to classify the fuel. For proof-of-concept purposes, engine input-output responses were measured for three different fuels at three different engine operating conditions. Two neural-network-based fuel classifiers were developed for different classification scenarios. Of the three engine operating conditions tested, two conditions were selected for the fuel classifier to be active.

Alternative combustion crossing stoichiometric air fuel ratio was investigated to utilize a 4-way catalyst system with LNT (lean NOx trap). The chemical mechanism of restricting soot formation reactions with low combustion temperature was combined with the physical mechanism of reducing smoke by lowering local equivalence ratio to enable low smoke rich and near rich combustion. A new combustion chamber for spatially and timely mixture formation phasing was developed to combine the two mechanisms and allow smooth EGR changing over a wide load range. Through this investigation, rich and near rich combustion to effectively utilize a 4-way catalyst system was realized. In addition, conditions suitable for LNT sulfur regeneration were realized from light to medium load.

SAE Recommended Practice J1540 [1] specifies test procedures to map transmission efficiency and parasitic losses in a manual transmission. The procedure comprises two parts. The first compares input and output torque over a range of speed to determine efficiency. The second measures parasitic losses at zero input torque over a range of speed. As specified in J1540, efficiency of transmissions is routinely measured on a test-stand under steady torque and speed [2] [3]. While such testing is useful to compare different transmissions, it is unclear whether the “in-use” efficiency of a given transmission is the same as that measured on the stand. A vehicular transmission is usually mated to a reciprocating combustion engine producing significant torque and speed fluctuations at the crankshaft. It is thus a valid question whether the efficiency under such pulsating conditions is the same as that under steady conditions.

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.

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.

Cooled Exhaust Gas Recirculation (EGR) technology provides significant benefits such as better cycle efficiency, knock tolerance and lower NOx/PM emissions. However, EGR dilution also poses challenges in terms of combustion stability, power density and control. Conventional control schemes for EGR engines rely on a differential pressure sensor combined with an orifice flow model to estimate EGR flow rate. While EGR rate is an important quantity, intake O2 mass fraction may be a better indication of EGR, capturing quantity as well as “quality” of EGR. SwRI has successfully used intake O2 mass fraction as a controlled state to manage several types of EGR engines - dual loop EGR diesel engines, low pressure loop /dedicated EGR (D-EGR) gasoline engines as well as dual fuel engines. Several suppliers are currently developing intake O2 sensors but they typically suffer from limited accuracy, response time and reliability. Also, addition of a new sensor implies increased production costs.

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.

The CO2 advantage coupled with the low NOX and PM potential of natural gas (NG) makes it well-suited for meeting future greenhouse gas (GHG) and NOX regulations for on-road medium and heavy-duty engines. However, because NG is mostly methane, reduced combustion efficiency associated with traditional NG fueling strategies can result in significant levels of methane emissions which offset the CO2 advantage due to reduced efficiency and the high global warming potential of methane. To address this issue, the unique co-direct injection capability of the Westport HPDI fuel system was leveraged to obtain a partially-premixed fuel charge by injecting NG during the compression stroke followed by diesel injection for ignition timing control. This combustion strategy, referred to as DI2, was found to improve thermal and combustion efficiencies over fumigated dual-fuel combustion modes.

For the US market, an abundant supply of natural gas (NG) coupled with recent green-house gas (GHG) regulations have spurred renewed interest in dual-fuel combustion regimes. This paper explores the potential of co-direct injection to improve the efficiency and reduce the methane emissions versus equivalent fumigated dual-fuel combustion systems. Using the Westport HPDI engine as the experimental test platform, the paper reports the results obtained using both diffusion controlled (HPDI) combustion strategy as well as a partially-premixed combustion strategy (DI2). The DI2 combustion strategy shows good promise, as it has been found to improve the engine efficiency by over two brake thermal efficiency (BTE) points (% fuel energy) compared to the diffusion controlled combustion strategy (HPDI) while at the same time reducing the engine-out methane emissions by 75% compared to an equivalent fumigated dual-fuel combustion system.

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.

In 1997 the US EPA formed a Heavy-Duty Engine Working Group (HDEWG) in the Mobile Sources Technical Advisory Subcommittee to address the questions related to fuel property effects on heavy-duty diesel engine emissions. The Working Group consisted of members from EPA and the oil refining and engine manufacturing industries. The goal of the Working Group was to help define the role of the fuel in meeting the future emissions standards in advanced technology engines (beyond 2004 regulated emissions levels). To meet this objective a three-phase program was developed. Phase I was designed to demonstrate that a prototype engine, located at Southwest Research Institute, represented similar emissions characteristics to that of certain manufacturers prototype engines. Phase II was designed to document the effects of selected fuel properties using a statistically designed fuel matrix in which cetane number, density, and aromatic content and type were the independent variables.

The effects of downspeeding and supercharging a passenger car diesel engine were studied through laboratory investigation and vehicle simulation. Changes in the engine operating range, transmission gearing, and shift schedule resulted in improved fuel consumption relative to the baseline turbocharged vehicle while maintaining performance and drivability metrics. A shift schedule optimization technique resulted in fuel economy gains of up to 12% along with a corresponding reduction in transmission shift frequency of up to 55% relative to the baseline turbocharged configuration. First gear acceleration, top gear passing, and 0-60 mph acceleration of the baseline turbocharged vehicle were retained for the downsped supercharged configuration.

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.

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.

In June of 2015, the Environmental Protection Agency and the National Highway Traffic Safety Administration issued a Notice of Proposed Rulemaking to further reduce greenhouse gas emissions and improve the fuel efficiency of medium- and heavy-duty vehicles. The agencies proposed that vehicle manufacturers would certify vehicles to the standards by using the agencies’ Greenhouse Gas Emission Model (GEM). The agencies also proposed a steady-state engine test procedure for generating GEM inputs to represent the vehicle’s engine performance. In the proposal the agencies also requested comment on an alternative engine test procedure, the details of which were published in two separate 2015 SAE Technical Papers [1, 2]. As an alternative to the proposed steady-state engine test procedure, these papers presented a cycle-average test procedure.

With the conclusion of the California Air Resources Board (CARB) Stage 1 Ultra-Low NOX (ULN) program, there continues to be a commitment for identifying potential pathways to demonstrate 0.02 g/bhp-hr NOX emissions. The Stage 1 program focused on achieving the ULN levels on the heavy-duty regulatory cycles utilizing a turbo-compound engine which required the integration of novel catalyst technologies and a supplemental heat source. While the aftertreatment configuration provided a potential solution to meet the ULN target, a complicated approach with a greenhouse gas (GHG) penalty was required to overcome challenges from low temperature exhaust. A subsequent Stage 2 program was concerned with the development of a new low load test cycle and evaluating the trade-off between GHG and tailpipe NOX on the Stage 1 ULN solution.