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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.

Octane appetite of modern engines has changed as engine designs have evolved to meet performance, emissions, fuel economy and other demands. The octane appetite of seven modern vehicles was studied in accordance with the octane index equation OI=RON-KS, where K is an operating condition specific constant and S is the fuel sensitivity (RONMON). Engines with a displacement of 2.0L and below and different combinations of boosting, fuel injection, and compression ratios were tested using a decorrelated RONMON matrix of eight fuels. Power and acceleration performance were used to determine the K values for corresponding operating points. Previous studies have shown that vehicles manufactured up to 20 years ago mostly exhibited negative K values and the fuels with higher RON and higher sensitivity tended to perform better.

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.

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.).

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.