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In response to global climate change, there is a widespread push to reduce carbon emissions in the transportation sector. For the difficult to decarbonize heavy-duty (HD) vehicle sector, lower carbon intensity fuels can offer a low-cost, near-term solution for CO2 reduction. The use of natural gas can provide such an alternative for HD vehicles while the increasing availability of renewable natural gas affords the opportunity for much deeper reductions in net-CO2 emissions. With this in consideration, the US National Renewable Energy Laboratory launched the Natural Gas Vehicle Research and Development Project to stimulate advancements in technology and availability of natural gas vehicles. As part of this program, Southwest Research Institute developed a hybrid-electric medium-HD vehicle (class 6) to demonstrate a substantial CO2 reduction over the baseline diesel vehicle and ultra-low NOx emissions.

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.

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.

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.

The short-term future direction of the automotive transportation sector is uncertain. Many governments and environmental localities around the world are proposing internal combustion engine (ICE) bans and enacting large subsidy programs for zero-tailpipe emissions vehicles powered by batteries or fuel-cells. Such policies can be effective in driving the consumer towards specific powertrains. The reason for such aggressive change is to reduce the sector’s carbon footprint. However, it is not clear if these proposals will reduce greenhouse gas (GHG) emissions. Emissions from raw material extraction, manufacturing, and power generation are shadowed by the focus on reducing the reliance on fossil fuel use. Emissions from non-tailpipe sources should also be considered before pushing for a rapid change to powertrains. Life-cycle analysis (LCA) can assess the GHG emissions produced before, during and after the life of a vehicle in a cradle-to-grave analysis.

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.

Range-extended hybrids are an attractive option for medium- and heavy-duty commercial vehicle fleets because they offer the efficiency of an electrified powertrain with the driving range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy (DOE)-funded project for Medium- and Heavy-Duty Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric Class 6 truck with a range-extending engine designed for pickup and delivery application.

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.

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.

Understanding the complex and dynamic nature of wheel-loader's operation requires a detailed system model. This paper describes the development of a conventional wheel-loader's system model that can be used to evaluate the transient response. The model includes engine details such as a mean value engine model, which takes into account turbocharger dynamics and engine governor controller. This allows the model to predict realistic performance and fuel consumption over a drive cycle. The wheel-loader machine is modeled in LMS Amesim® and the engine governor controller is modeled in Matlab/SIMULINK®. In order to simplify the model, hydraulic loads from the boom / bucket mechanism, steering and cooling fan are modeled as hydraulic load inputs obtained from typical short V-drive cycle. Critical wheel-loader drive cycle inputs into the model have been obtained from testing and have been used to validate the system response and cycle fuel consumption.

In August of 2011, the US Environmental Protection Agency issued new Green House Gas (GHG) emissions regulations for heavy duty vehicles. These regulations included new procedures for the evaluation of hybrid powertrains and vehicles. One of the hybrid options allows for the evaluation of an engine plus a hybrid transmission (a powertrain). For this type of testing, EPA has proposed simulating a vehicle following the hybrid vehicle test procedures, including the use of the vehicle cycles and the A to B comparison testing - as required for the full vehicle evaluation option. This paper proposes an alternative approach by defining a powertrain cycle. The powertrain cycle is based on the heavy duty engine emissions cycle - the transient FTP cycle. Simulation and test results are presented showing similar performance over the engine and vehicle cycles. This approach offers several advantages as compared to the procedure described in EPA's GHG rule.

A range of cycle characteristics have been used to estimate the hybrid potential for vehicle duty cycles including characteristic acceleration, aerodynamic velocity, kinetic intensity, stop time, etc. These parameters give an indication of overall hybrid potential benefits, but do not contain information on the distribution of the available braking energy and the hybrid system power required to capture the braking energy. In this paper, the authors propose two new cycle characteristics to help evaluate overall hybrid potential of vehicle cycles: P50 and P90, which are non-dimensional power limits at 50% and 90% of available braking energy. These characteristics are independent of vehicle type, and help illustrate the potential hybridization benefit of different drive cycles. First, the distribution of available braking energy as a function of brake power for different vehicle cycles and vehicle classes is analyzed.