Search Results

As GHG and fuel economy regulations of light-duty vehicles have become more stringent, advanced emissions reduction technology has extensively penetrated the US light-duty vehicle fleet. This new technology includes not only advanced conventional engines and transmissions, but also greater adoption of electrified powertrains. In 2022, electrified vehicles – including mild hybrids, strong hybrids, plug-ins, and battery electric vehicles – made up nearly 17% of the US fleet and are on track to further increase their proportion in subsequent years. The Environmental Protection Agency (EPA) has previously used its Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) full vehicle simulation tool to evaluate the greenhouse gas (GHG) emissions of light-duty vehicles. ALPHA contains a library of benchmarked powertrain components that can be matched to specific vehicles to explore GHG emissions performance.

Since the early 1990’s, commercial vehicles have suffered from repeated vulnerability exploitations that resulted in a need for improved automotive cybersecurity. This paper outlines the strategies and challenges of implementing an automotive Zero Trust Architecture (ZTA) to secure intra-vehicle networks. Zero Trust (ZT) originated as an Information Technology (IT) principle of “never trust, always verify”; it is the concept that a network must never assume assets can be trusted regardless of their ownership or network location. This research focused on drastically improving security of the cyber-physical vehicle network, with minimal performance impact measured as timing, bandwidth, and processing power. The automotive ZTA was tested using a software-in-the-loop vehicle simulation paired with resource constrained hardware that closely emulated a production vehicle network.

Shafaat and Kenley in 2015 identified the opportunity to improve System Engineering Standards by incorporating the design principle of learning. The System Level Assessment (SLA) Methodology is an approach that fulfills this need by efficiently capturing the learnings of a team of subject matter experts in the early stages of product system design. By gathering expertise, design considerations are identified that when used with market and business requirements improve the overall quality of the product system. To evaluate the effectiveness of this approach, the methodology has been successfully applied over 400 times within each realm of the New Product Introduction process, including most recently to a Technology Development program (in the earliest stages of the design process) to assess the viability of various electrification technologies under consideration by an automotive Tier 1 supplier.

Simulation and testing are often done by different engineers in different departments of a company. This can lead to disconnects and unrealistic predictions, especially if the person doing simulations does not have an experimental background. On the other hand, experimental results can also include errors that result in misleading answers. It is important for the engineer doing either testing or simulation to have a good understanding for what results are plausible and what results might be suspect. This paper will provide examples where error crept into testing or simulation that could have been caught and corrected early if a good feel for “reasonable” results had been in place. The importance of understanding how a software package is analyzing the data will be explained, since settings buried deep within a menu structure can drive misleading results.

Trained human raters have been used by organizations such as the Coordinating Research Council (CRC) to assess the vehicle driveability performance effect of fuel volatility. CRC conducts workshops to test fuel effects and their impact on vehicle driveability. CRC commissioned Southwest Research Institute (SwRI) to develop a “Trick Car” vehicle that could trigger malfunctions on-demand that mimic driveability events. This vehicle has been used to train novice personnel on the CRC Driveability Procedure E-28-94. While largely effective, even well-trained human raters can be inconsistent with other raters. Further, CRC rater workshop programs used to train and calibrate raters are infrequent, and there are a limited number of available trained raters. The goal of this program was to augment or substitute human raters with an electronic driveability sensing system.

The size and distribution of a vehicle’s tailpipe particulate emissions can have a strong impact on human health, especially if the particles are small enough to enter the human respiratory system. Gasoline direct injection (GDI) has been adopted widely to meet stringent fuel economy and CO2 regulations across the globe for recent engine architectures. However, the introduction of GDI has led to challenges concerning the particulate matter (PM) and particle number (PN) emissions from such engines. This study aimed to compare the particulate emissions of three SI combustion strategies: conventional SI, conventional stoichiometric low-pressure exhaust gas recirculation (LP-EGR), and Dedicated-EGR (D-EGR) at four specific test conditions. It was shown that the engine-out PM/PN for both the EGR strategies was lower than the conventional SI combustion under normal operating conditions. The test conditions were chosen to represent the WLTC test conditions.

The Exhaust Composition Transient Operation LaboratoryTM (ECTO-LabTM) is a burner system developed at Southwest Research Institute (SwRI) for simulation of IC engine exhaust. The current system design requires metering and combustion of nitromethane in conjunction with the primary fuel source as the means of NOX generation. While this method affords highly tunable NOX concentrations even over transient cycles, no method is currently in place for dictating the speciation of nitric oxide (NO) and nitrogen dioxide (NO2) that constitute the NOX mixture. NOX generated through combustion of nitromethane is dominated by NO, and generally results in an NO2:NOX ratio of < 5 %. Generation of any appreciable quantities of NO2 is therefore dependent on an oxidation catalyst to oxidize a fraction of the NO to NO2.

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.

Fuel usage negatively impacts the environment and is a significant portion of operational costs of moving freight globally. Reducing fuel consumption is key to lessening environmental impacts and maximizing freight efficiency, thereby increasing the profit margin of logistic operators. In this paper, fuel economy improvements of a cab-over style 49T heavy duty Foton truck powered by a Cummins 12-liter engine are studied and systematically applied for the China market. Most fuel efficiency improvements are found within the vehicle design when compared to opportunities available at the engine level. Vehicle design (improved aerodynamics), component selection/matching (low rolling resistance tires), and powertrain electronic features integration (shift schedule/electronic trim) offer the largest opportunities for lowering fuel consumption.

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.

In modern engine design, downsizing and reducing weight while still providing an increased amount of power has been a general trend in recent decades. Traditionally, an engine design with superior NVH performance usually comes with a heavier, thus sturdier structure. Therefore, modern engine design requires that NVH be considered in the very early design stage to avoid modifications of engine structure at the last minute, when very few changes can be made. NVH design optimization of engine components has become more practical due to the development of computer software and hardware. However, there is still a need for smarter algorithms to draw a direct relationship between the design and the radiated sound power. At the moment, techniques based on modal acoustic transfer vectors (MATVs) have gained popularity in design optimization for their good performance in sound pressure prediction.

This paper presents the fuel consumption results of engine and vehicle simulation modeling for a wide variety of individual technologies and technology packages applied to a long haul heavy duty vehicle. Based on the simulation modeling, up to 11% in fuel savings is possible using commercially available and emerging technologies applied to a 15L DD15 engine alone. The predicted fuel savings are up to 17% in a Kenworth T700 tractor-trailer unit equipped with a range of vehicle technologies, but using the baseline DD15 diesel engine. A combination of the most aggressive engine and vehicle technologies can provide savings of up to 29%, averaged over a range of drive cycles. Over 30% fuel savings were found with the most aggressive combination on a simulated long haul duty cycle. Note that not all of these technologies may prove to be cost-effective. The fuel savings benefits for individual technologies vary widely depending on the drive cycles and payload.

Simulations used to estimate carbon dioxide (CO2) emissions and fuel consumption of medium- and heavy-duty vehicles over prescribed drive cycles often employ engine fuel maps consisting of engine measurements at numerous steady-state operating conditions. However, simulating the engine in this way has limitations as engine controls become more complex, particularly when attempting to use steady-state measurements to represent transient operation. This paper explores an alternative approach to vehicle simulation that uses a “cycle average” engine map rather than a steady state engine fuel map. The map contains engine CO2 values measured on an engine dynamometer on cycles derived from vehicle drive cycles for a range of generic vehicles. A similar cycle average mapping approach is developed for a powertrain (engine and transmission) in order to show the specific CO2 improvements due to powertrain optimization that would not be recognized in other approaches.

Medium- and Heavy Duty Truck fuel consumption and the resulting greenhouse gas (GHG) emissions are significant contributors to overall U.S. GHG emissions. Forecasts of medium- and heavy-duty vehicle activity and fuel use predict increased use of freight transport will result in greatly increased GHG emissions in the coming decades. As a result, the National Highway Traffic Administration (NHTSA) and the United States Environmental Protection Agency (EPA) finalized a regulation requiring reductions in medium and heavy truck fuel consumption and GHGs beginning in 2014. The agencies are now proposing new regulations that will extend into the next decade, requiring additional fuel consumption and GHG emissions reductions. To support the development of future regulations, a research project was sponsored by NHTSA to look at technologies that could be used for compliance with future regulations.

This paper presents the results of engine and vehicle simulation modeling for a wide variety of individual technologies and technology packages applied to two medium-duty vocational vehicles. Simulation modeling was first conducted on one diesel and two gasoline medium-duty engines. Engine technologies were then applied to the baseline engines. The resulting fuel consumption maps were run over a range of vehicle duty cycles and payloads in the vehicle simulation model. Results were reported for both individual engine technologies and combinations or packages of technologies. Two vehicles, a Kenworth T270 box delivery truck and a Ford F-650 tow truck were evaluated. Once the baseline vehicle models were developed, vehicle technologies were added. As with the medium-duty engines, vehicle simulation results were reported for both individual technologies and for combinations. Vehicle technologies were evaluated only with the baseline 2019 diesel medium-duty engine.

Several wireless systems such as Dedicated Short Range Communication (DSRC), cellular, Wi-Fi, Bluetooth, and the Tire Pressure Monitoring System (TPMS) can be found on modern vehicles. In the future, Software Defined Radio (SDR) technology could be integrated into automobiles to increase the efficiency and adaptability of wireless communications systems. SDR is also a powerful tool for designing and testing new communications protocols. However there are also some security considerations associated with SDR. This paper will review some advantages of using SDR technology in the automotive domain as well as potential security issues. The authors are currently conducting research into the use of SDR technology to model wireless systems and investigate security threats in modern vehicular systems.

The State of Florida initiated the SunGuide® Advanced Traffic Management System (ATMS) software development program in October 2003; that software is deployed in over 15 Traffic Management Centers (TMCs) throughout the major cities in Florida. One of SunGuide's biggest strengths is a software architecture that allows for short development times for new functionality. This paper describes the system design, implementation, and lessons learned from the development of the SunGuide Connected Vehicle Subsystem (CVS). The SunGuide CVS receives real-time speed, location, and heading data from instrumented vehicles and uses that data in a manner similar to its use of traffic detector data to provide information to the TMC. In addition to the vehicle providing data to the TMC, the TMC may create and publish Traveler Advisory Messages to the vehicle as part of the SunGuide Event Management response plans.

This paper describes advances in variable speed supercharging, including benefits for both fuel economy and low speed torque improvement. This work is an extension of the work described in SAE Paper 2012-01-0704 [8]. Using test stand data and state-of-the-art vehicle simulation software, a NuVinci continuously variable planetary (CVP) transmission driving an Eaton R410 supercharger on a 2.2 liter diesel was compared to the same base engine/vehicle with a turbocharger to calculate vehicle fuel economy. The diesel engine was tuned for Tier 2 Bin 5 emissions. Results are presented using several standard drive cycles. A Ford Mustang equipped with a 4.6 liter SI engine and prototype variable speed supercharger has also been constructed and tested, showing low speed torque increases of up to 30%. Dynamometer test results from this effort are presented. The combined results illustrate the promise of variable speed supercharging as a viable option for the next generation of engines.

Selective catalytic reduction (SCR) catalysts will be used to reduce oxides of nitrogen (NOx) emissions from internal combustion engines in a number of applications [1,2,3,4]. Southwest Research Institute® (SwRI)® performed an Internal Research & Development project to study SCR catalyst thermal deactivation. The study included a V/W/TiO2 formulation, a Cu-zeolite formulation and an Fe-zeolite formulation. This work describes NOx timed response to ammonia (NH3) transients as a function of thermal aging time and temperature. It has been proposed that the response time of NOx emissions to NH3 transients, effected by changes in diesel emissions fluid (DEF) injection rate, could be used as an on-board diagnostic (OBD) metric. The objective of this study was to evaluate the feasibility and practicality of this OBD approach.

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.