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The diesel engine can be an effective solution to meet future greenhouse gas and fuel economy standards, especially for larger segment vehicles. However, a key challenge facing the diesel is the upcoming LEV III and Tier 3 emission standards which will require significant reductions in hydrocarbon (HC) and oxides of nitrogen (NOx) emissions. The challenge stems from the fact that diesel exhaust temperatures are much lower than gasoline engines, so the time required to achieve effective emissions control after a cold-start with typical aftertreatment devices is considerably longer. To address this challenge, a novel diesel cold-start emission control strategy was investigated on a 2L class diesel engine. This strategy combines several technologies to reduce tailpipe HC and NOx emissions before the start of the second hill of the FTP75. The technologies include both engine tuning and aftertreatment changes.

The California Air Resources Board (CARB)-funded Stage 3 Heavy-Duty Low NOX program focusses on evaluating different engine and after-treatment technologies to achieve 0.02g/bhp-hr of NOX emission over certification cycles. This paper highlights the controls architecture of the engine and after-treatment systems and discusses the effects of various strategies implemented and tested in an engine test cell over various heavy-duty drive cycles. A Cylinder De-Activation (CDA) system enabled engine was integrated with an advanced after-treatment controller and system package. Southwest Research Institute (SwRI) had implemented a model-based controller for the Selective Catalytic Reduction (SCR) system in the CARB Stage 1 Low-NOX program. The chemical kinetics for the model-based controller were further tuned and implemented in order to accurately represent the reactions for the catalysts used in this program.

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.

A new diesel engine system adopting alternative combustion with rich and near rich combustion, and an airflow-dominant control system for precise combustion control was used with a 4-way catalyst system with LNT (lean NOx trap) to achieve Tier II Bin 5 on a 2.2L TDI diesel engine. The study included catalyst temperature control, NOx regeneration, desulfation, and PM oxidation with and without post injection. Using a mass-produced lean burn gasoline LNT with 60,000 mile equivalent aging, compliance to Tier II Bin 5 emissions was confirmed for the US06 and FTP75 test cycles with low NVH, minor fuel penalty and smooth transient operation.

An airflow-dominant control system was developed to provide precise engine and exhaust treatment control with low air fuel ratio alternative combustion. The main elements of the control logic include a real-time state observer for in-cylinder oxygen mass estimation, a simplified packaging scheme for all air-handling and fueling parameters, a finite state machine for control mode switching, combustion control models to maintain robust alternative combustion during transients, and smooth rich/lean switching during lean NOx trap (LNT) regeneration without post injection. The control logic was evaluated on a passenger car equipped with a 4-way catalyst system with LNT and was instrumental in achieving US Tier II Bin 5 emission targets with good drivability and low NVH.

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.

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.).

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.

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.

Conventional methods for determining automotive powertrain efficiency include (1) component-level testing, such as engine dynamometer, transmission stand or axle stand testing, (2) simulations based on component level test data and (3) vehicle-level testing, such as chassis dynamometer or on-road testing. This paper focuses on vehicle-level testing to show where energy is lost throughout a complete vehicle powertrain. This approach captures all physical effects of a vehicle driving in real-world conditions, including torque converter lockup strategies, transmission shifting, engine control strategies and inherent mechanical efficiency of the components. A modern rear-wheel drive light duty pickup truck was instrumented and tested on a chassis dynamometer. Power was measured at the engine crankshaft output, the rear driveshaft and at the dynamometer.

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 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.

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.

This work is a part of medium-duty Low NOx technology development project with a focus on evaluating a combination of engine and advanced aftertreatment for 0.02 g/bhp-hr NOx regulation proposed by CARB (California air resource board). In this project, a control oriented chemical kinetics model of SCR (Selective catalytic reduction) was used in the aftertreatment controller that is susceptible to performance degradation due to hydrothermal and chemical aging. This paper focuses on modeling the NOx conversion and NH3 storage characteristics using a controls oriented SCR plant model which is further used for a model-based urea dosing scheme. A set of steady state reactor tests were used to calibrate the SCR performance at degreened, hydrothermal only and hydrothermal + chemical aging conditions and also to determine inhibition factors related to aging. The resultant model is capable of simulating SCR performance deterioration such as a reduction in NOx conversion and NH3 storage.

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.

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.

Typical two-site storage-based SCR plant models in literature consider NH3 stored in the first site to participate in NH3 storage, NOx conversion and second site to only participate in NH3 storage passively. This paper focuses on quantifying the impact of stored NH3 in the second site on the overall NOx conversion for an ultra-low NOx system due to intra site NH3 mass transfer. Accounting for this intra site mass transfer leads to better prediction of SCR out NH3 thus ensuring compliance with NH3 coverage targets and improved dosing characteristics of the controller that is critical to achieving ultra-low NOx standard. The stored NH3 in the second site undergoes mass transfer to the first site during temperature ramps encountered in a transient cycle that leads to increased NOx conversion in conditions where the dosing is switched off. The resultant NH3 coverage fraction prediction is critical in dosing control of SCR.

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.