Search Results

Efficient and accurate ordinary differential equation (ODE) solvers are necessary for powertrain and vehicle dynamics modeling. However, current commercial ODE solvers can be financially prohibitive, leading to a need for accessible, effective, open-source ODE solvers designed for powertrain modeling. Rust is a compiled programming language that has the potential to be used for fast and easy-to-use powertrain models, given its exceptional computational performance, robust package ecosystem, and short time required for modelers to become proficient. However, of the three commonly used (>3,000 downloads) packages in Rust with ODE solver capabilities, only one has more than four numerical methods implemented, and none are designed specifically for modeling physical systems. Therefore, the goal of the Differential Equation System Solver (DESS) was to implement accurate ODE solvers in Rust designed for the component-based problems often seen in powertrain modeling.

The widely accepted best practice for spark-ignition combustion is the four-valve pent-roof chamber using a central sparkplug and incorporating tumble flow during the intake event. The bulk tumble flow readily breaks up during the compression stroke to fine-scale turbulent kinetic energy desired for rapid, robust combustion. The natural gas engines used in medium- and heavy-truck applications would benefit from a similar, high-tumble pent-roof combustion chamber. However, these engines are invariably derived from their higher-volume diesel counterparts, and the production volumes are insufficient to justify the amount of modification required to incorporate a pent-roof system. The objective of this multi-dimensional computational study was to develop a combustion chamber addressing the objectives of a pent-roof chamber while maintaining the flat firedeck and vertical valve orientation of the diesel engine.

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.

Engine knock is one of the limiting factors in determining the compression ratio and engine efficiency for spark ignited engines. Using the Southwest Research Institute Knock-CoV test method, it was previously shown that the knock limited load versus combustion phasing (CA50) has a constant slope. All of the knock mitigation strategies tested provided a shift to these knock limited loads but also increased the slope. That is, for the same CA50 retard the knock limited load could be increased more. Our hypothesis was that due to fuel sensitivity, or the difference between the RON and MON, the reactions that lead to knock will behave differently as the pressure-temperature history changes with engine speeds and loads. The fuel affects on the knock and CoV limits were studied by testing fuels with various sensitivities including methanol, E85 (85% ethanol) and Iso-octane.

According to the Annual Energy Outlook 2022 (AEO2022) report, almost 30% of the transport sector will still use internal combustion engines (ICE) until 2050. The transportation sector has been actively seeking different methods to reduce the CO2 emissions footprint of fossil fuels. The use of lower carbon-intensity fuels such as Renewable Diesel (RD) can enable a pathway to decarbonize the transport industry. This suggests the need for experimental or advanced numerical studies of RD to gain an understanding of its combustion and emissions performance. This work presents a numerical modeling approach to study the combustion and emissions of RD. The numerical model utilized the development of a reduced chemical kinetic mechanism for RD’s fuel chemistry. The final reduced mechanism for RD consists of 139 species and 721 reactions, which significantly shortened the computational time from using the detailed mechanism.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

Conventional diesel combustion (CDC) is known to provide high efficiency and reliable engine performance, but often associated with high particulate matter (PM) and nitrogen oxides (NOX) emissions. Combustion of fossil diesel fuel also produces carbon dioxide (CO2), which acts as a harmful greenhouse gas (GHG). Renewable and low-carbon fuels such as renewable diesel (RD) and methanol can play an important role in reducing harmful criteria and CO2 emissions into the atmosphere. This paper details an experimental study using a single-cylinder research engine operated under dual-fuel combustion using methanol and RD. Various engine operating strategies were used to achieve diesel-like fuel efficiency. Measurements of engine-out emissions and in-cylinder pressure were taken at test conditions including low-load and high-load operating points.

Low carbon emissions policies for the transportation sector have recently driven more interest in using low net-carbon fuels, including biodiesel. An internal combustion engine (ICE) can operate effectively using biodiesel while achieving lower engine-out emissions, such as soot, mostly thanks to oxygenate content in biodiesel. This study selected a heavy-duty (HD) single-cylinder engine (SCE) platform to test biodiesel fuel blends with 20% and 100% biodiesel content by volume, referred to as B20, and B100. Test conditions include a parametric study of exhaust gas recirculating (EGR), and the start of injection (SOI) performed at low and high engine load operating points. In-cylinder pressure and engine-out emissions (NOX and soot) measurements were collected to compare diesel and biodiesel fuels.

Alternative fuels such as methanol can significantly reduce greenhouse gas (GHG) emissions when used in internal combustion engines (ICEs). This study characterized the combustion of methanol, methanol/diesel, and methanol/renewable diesel numerically. Numerical findings were also compared with engine experiments using a single-cylinder engine (SCE). The engine was operated under a dual-fuel combustion mode: methanol was fumigated at the intake port, and diesel was injected inside the cylinder. The characteristic of ignition delay trend as methanol concentration increased is being described at low temperature (low engine load) and high temperature (high engine load) conditions.

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.

Dedicated-EGR™ (D-EGR™) is a concept where the exhaust of one dedicated cylinder (D-Cyl) is routed into the intake thus producing EGR to be used by the whole engine. The D-Cyl operates rich of stochiometric which produces syngas that enhances the EGR stream permitting faster combustion and greater knock mitigation. Operating an engine using D-EGR improves the knock resistance which can permit a higher compression ratio (CR) thereby increasing efficiency. One challenge of traditional D-EGR is that the D-Cyl combustion becomes unstable operating with both rich and EGR dilute conditions. Therefore, the ‘Split Intake D-EGR’ concept seeks to resolve this problem by feeding fresh air to the D-Cyl, thus allowing even richer operation in the D-Cyl which further increases the H2 and CO yield thereby enhancing the efficiency benefits.

As an alternative fuel, renewable diesel (RD) could improve the performance of conventional internal combustion engines (ICE) because of its difference in fuel properties. With almost no aromatic content in the fuel, RD produces less soot emissions than diesel. The higher cetane number (CN) of RD also promotes ignition of the fuel, which is critical, especially under low load, and low reactivity conditions. This study tested RD fuel in a heavy-duty single-cylinder engine (SCE) under compression-ignition (CI) operation. Test condition includes low and high load points with change in exhaust gas recirculation (EGR) and start of injection (SOI). Measurements and analysis are provided to study combustion and emissions, including particulate matters (PM) mass and particle number (PN). It was found that while the combustion of RD and diesel are very similar, PM and PN emissions of RD were reduced substantially compared to diesel.

A physics-based spark ignition model was developed and integrated into a commercial CFD code. The model predicted the spark discharge process based on the electrical parameters of the secondary ignition circuit, tracked the spark motion as it was stretched by in-cylinder gas motion, and determined the resulting energy deposition to the gas. In concert with the existing kinetic solver in the CFD code, the resulting ignition and flame propagation processes were simulated. The model results have been validated against both imaging rig experiments of the spark in moving air and against engine experimental data. The model was able to replicate the key features of the spark and to capture the cyclic variability of high-dilution combustion when multiple engine cycles were simulated.

Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory’s FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls.

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory’s Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation.

Stringent emissions regulations and the need for lower tailpipe emissions are pushing the development of low-carbon alternative fuels. H2 is a zero-carbon fuel that has the potential to lower CO2 emissions from internal combustion engines (ICEs) significantly. Moreover, this fuel can be readily implemented in ICEs with minor modifications. Batteries can be argued to be a good zero tailpipe emission solution for the light-duty sector; however, medium and heavy-duty sectors are also in need of rapid decarbonization. Current strategies for H2 ICEs include modification of the existing spark ignition (SI) engines to run on port fuel injection (PFI) systems with minimal changes from the current compressed natural gas (CNG) engines. This H2 ICE strategy is limited by knock and pre-ignition. One solution is to run very lean (lambda >2), but this results in excessive boosting requirements and may result in high NOx under transient conditions.

The automotive sector is rapidly transitioning to decarbonized, electric vehicles solutions. However, due to challenges with such rapid adoption, Internal combustion engines (ICE) are expected to be used for decades to come. In this transition period it is important to continue to improve ICE efficiency. A key design parameter to increase ICE efficiency is the compression ratio. For gasoline engines, the compression ratio is limited so as to avoid knock. Engine designers can employ several strategies to mitigate knock and enable higher compression ratios. In this study, a new methodology has been developed to compare various knock mitigation strategies. By comparing the knock limited load at a given combustion phasing the expected compression ratio increase can be inferred.

This project’s objective was to generate experimental data to evaluate the impact of metals doped B20 on diesel particle filter (DPF) ash loading and performance compared to that of conventional petrodiesel. The effect of metals doped B20 vs. conventional diesel on a DPF was quantified in a laboratory controlled accelerated ash loading study. The ash loading was conducted on two DPFs – one using ULSD fuel and the other on B20 containing metals dopants equivalent to 4 ppm B100 total metals. Engine oil consumption and B20 metals levels were accelerated by a factor of 5, with DPFs loaded to 30 g/L of ash. Details of the ash loading experiment and on-engine DPF performance evaluations are presented in the companion paper (Part I). The DPFs were cleaned, and ash samples were taken from the cleaned material. X-ray Fluorescence (XRF), X-Ray Photoelectron Spectroscopy (XPS) and X-Ray Diffraction (XRD) were conducted on the ash samples.

Interaction between a diesel spray and piston plays a significant role in overall combustion and emissions performance in compression-ignition engines. It is essential to design the lip feature respective to spray targeting and the following charge motion for combustion systems that rely on spray-piston interaction strongly, such as a stepped-lip piston. This study used a numerical campaign using computational fluid dynamics (CFD) simulation to optimize a stepped-lip combustion system at a 22:1 compression ratio (CR) for both performance and emissions. This is a substantial step up in CR from the stock value of 17:1 for the same engine platform. A machine learning model was used to identify the best combination of features from a design space involving hundreds of potential piston designs and injector nozzle configurations. This study provides a discussion on the general combustion characteristics of the stepped-lip combustion system and the sensitivity of the design parameters.

Heavy-duty (HD) internal combustion engines (ICE) have achieved quite high brake thermal efficiencies (BTE) in recent years. However, worldwide GHG regulations have increased the pace towards zero CO2 emissions. This, in conjunction with the ICE reaching near theoretical efficiencies means there is a fundamental lower limit to the GHG emissions from a conventional diesel engine. A large factor in achieving lower GHG emissions for a given BTE is the fuel, in particular its hydrogen to carbon ratio. Substituting a fuel like diesel with compressed natural gas (CNG) can provide up to 25% lower GHG at the same BTE with a sufficiently high substitution rate. However, any CNG slip through the combustion system is penalized heavily due to its large global warming potential compared to CO2. Therefore, new technologies are needed to reduce combustion losses in CNG-diesel dual fuel engines.