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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.

This paper describes an in-cylinder lean-rich combustion (no-post-injection for rich) switching control approach for modern diesel engines equipped with exhaust-treatment systems. No-post-injection rich combustion is desirable for regeneration of engine exhaust-treatment systems thanks to its less fuel penalty compared with regeneration approaches using post-injections and / or in-exhaust injections. However, for vehicle applications, it is desirable to have driver-transparent exhaust-treatment system regenerations, which challenge the in-cylinder rich-lean combustion transitions. In this paper, a nonlinear in-cylinder condition control system combined with in-cylinder condition guided fueling control functions were developed to achieve smooth in-cylinder lean-rich switching control at both steady-state and transient operation. The performance of the control system is evaluated on a modern light-duty diesel engine (G9T600).

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.

This paper describes a hybrid robust nonlinear control approach for modern diesel engines running low temperature combustion and conventional diesel combustion modes. Using alternative combustion modes has become a promising approach to reduce engine emissions. However, due to very different in-cylinder conditions and fueling parameters for different combustion modes, control of engines operating multiple combustion modes is very challenging. It becomes difficult for conventional calibration / mapping based approaches to produce satisfactory results in terms of engine torque responses and emissions. Advanced control techniques are then demanded to accomplish the tasks. An innovative hybrid control system is designed to track different key engine operating variables at different combustion modes as well as avoid singularity which is inherent for turbocharged diesel engines running multiple combustion modes.

This paper describes algorithms that can recognize and track the engine crankshaft position for arbitrary cam- and crank-shaft tooth wheel patterns in both steady-state and transient operating conditions. Crankshaft position tracking resolution is adjustable to accommodate different application requirements. The instantaneous crankshaft position information provided by the position tracking module form the basis for crankshaft angle domain (CAD) engine control and measurement functions such as precise injection / ignition controls and on-line cylinder pressure CAD analyses. The algorithms described make reconfiguration of the tracking module for different and arbitrary cam- and crank-shaft tooth wheel patterns very easy, which is valuable especially for prototyping engine control systems. The effectiveness of the algorithms is shown using test engines with different cam and crank signal patterns.

Alternative combustion modes such as homogeneous charge compression ignition (HCCI) and low temperature combustion (LTC) have shown very promising engine-out emissions. However, these combustion modes are close to the combustion stability boundaries and very sensitive to in-cylinder condition variations. Thus, transient control of engines operating in alternative combustion modes are very challenging compared with control of engines with conventional combustion mode only. This paper presents some advanced air-path control and estimation techniques/practice for multiple and alternative combustion mode engine transient operation. Based on the mean-value engine dynamic models, robust controllers can be designed to track the desired air-path performance variables to ensure desirable combustion during transient operation.

In-cylinder ion sensing through sparkplug electrodes can be used to determine in-cylinder A/F ratio by using a modified production coil-on-plug ignition system having ion sensing capability. The in-cylinder ionization can be characterized by the height of the peak, location of the peak from ignition command and area under the ionization signal curve. The effects of A/F ratio on the in-cylinder ionization can be isolated from other affecting factors by conducting tests on a constant volume combustion device in which the initial pressure and temperature can be well controlled. This results in a parabolic correlation of the ionization characteristics with the mixture equivalence ratio. Additionally the ionization characteristics show strong dependence on engine load and speed. Equivalence ratio characteristics during engine cranking and warm up are investigated, and a method for on-line calibration of ionization detection is discussed.

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.

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.

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.

Tremendous amount of useful information can be extracted from the cylinder pressure signal for engine combustion control. However, the physical cylinder pressure sensors are undesirably expensive and their health need to be monitored for fault diagnostic purpose as well. This paper presents the results of the development of a virtual cylinder pressure sensor (VCPS) with individual variable-oriented independent estimators. Two neural network-based independent cylinder pressure related variable estimators were developed and verified at steady state. The results show that these models can predict the variables correctly compared with the extracted variables from the measured physical cylinder pressure sensor signal. Good generalization capabilities of the developed models are observed in the sense that the models work well not only for the training data set but also for the new inputs that they have never been exposed to before.

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.