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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.

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.

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.

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.

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.

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.

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.

A comprehensive 3D Computational Fluid Dynamics (CFD) with conjugate heat transfer (CHT) tool was developed in-house for a Tesla Model 3 electric motor. To accurately predict the power loss (heat generation) inside the electric motor, the electromagnetic process was solved to obtain the spatial-dependent power loss in the rotor, stator, and windings. CFD was utilized for simulating the coolant oil flow using the multiphase Volume of Fluid (VOF) approach and Finite Element Analysis (FEA) was used for simulating the thermal process within the solid domains. These three separate analysis modules (electromagnetic, fluid flow and thermal solid) were coupled strongly to enable two-way interactions. Thermal results obtained from the final converged simulations were compared to the test data obtained from the thermocouple measurements for the two most representative operating points of this e-motor and showed reasonable predictions with similar trend as observed in the test.

This paper describes a new method for measuring oil consumption using laser induced breakdown spectroscopy (LIBS). LIBS focuses a high energy laser pulse on a sample to form a transient plasma. As the plasma cools, each element produces atomic emission lines which can be used to identify and quantify the elements present in the original sample. In this work, a LIBS system was used on simulated engine exhaust with a focus on quantifying the inorganic components (termed ash) of the particulate emissions. Because some of the metallic elements in the ash almost exclusively result from lube oil consumption, their concentrations can also be correlated to an oil consumption rate. Initial testing was performed using SwRI’s Exhaust Composition Transient Operation Laboratory®(ECTO-Lab®) burner system so that oil consumption and ash mass could be precisely controlled.

Advanced diesel combustion systems continue to push the peak cylinder pressure limit of engines upward to allow high-efficiency combustion with high compression ratios (CR). The air-standard Otto and Diesel cycles indicate increased compression ratios lead to higher cycle efficiency. The study presented here describes the development and demonstration of a high-efficiency diesel combustion system. The study used both computational and experimental tools to develop the combustion system fully. Computational fluid dynamics (CFD) simulations were carried out to evaluate combustion with two combustion systems at a compression ratio of 22:1 with a Wave piston design (based on the production Volvo Wave piston). Analysis of combustion performance and emissions were performed to confirm the improvements these piston designs offered relative to the baseline combustion system for the engine. Companion single-cylinder engine (SCE) experiments were performed to validate the simulation results.

The transport of fuel-borne additives into the engine oil is a critical factor for the efficacy with which the additive functionality can be imparted on the engine. This paper describes the combination of Laser Induced Fluorescence (LIF) and Liquid Chromatography (LC) to determine the real-time additive concentrations and transfer ratios in a spark-ignition, 2-liter GM LHU engine. The current research used a continuous sample circuit from the engine sump which passed through an integrating cavity flow cell to enhance the LIF signal. In the absence of a fluorescence signature of any of the native additive species, a suitable fluorescing dye was selected to simulate the additive. After establishing rigorous calibration curves, LC was employed as a referee method to do a direct comparison with the LIF determined dye concentrations.

A push for more stringent emissions regulations has resulted in larger, increasingly complex aftertreatment solutions. In particular, oxides of nitrogen (NOX) and particulate matter (PM) have been controlled using two separate systems, selective catalytic reduction (SCR) and the catalyze diesel particulate filter (CDPF), or the functionality has been combined into a single device producing the SCR on filter (SCRoF). The SCRoF forgoes beneficial NO2 production present in the CDPF to avoid NH3 oxidation which occurs when using platinum group metals (PGM) for oxidation. In this study, mixed-metal oxides are shown to oxidize NO to NO2 without appreciable NH3 oxidation. This selectivity leads to enhanced performance when combined with a typical Cu-zeolite catalyst.

With the increasing level of electrification of on-road, off-road and stationary applications, use of larger lithium-ion battery packs has become essential. These packs require large capital investments on the order of millions of dollars and pose a significant risk of self-annihilation without rigorous safety evaluation and management. Testing these larger battery packs to validate design changes can be cost prohibitive. A reliable numerical simulation tool to predict battery thermal runaway under various abuse scenarios is essential to engineer safety into the battery pack design stage. A comprehensive testing & simulation workflow has been established to calibrate and validate the numerical modeling approach with the test data for each of the individual sub model - electrochemical, internal short circuit and thermal abuse model. A four-equation thermal abuse model was built and validated for lithium-ion 21700 form factor cylindrical cells using NCA cathodes.

SCR-on-filter, or SCRoF, is an emerging technology for different market segments and vehicle applications. The technology enables simultaneous particulate matter trapping and NOX reduction, and provides thermal management and aftertreatment packaging benefits. However, there is little information detailing the lubricant derived exposure effects on functional SCR performance. A study was conducted to evaluate the impact of various oil consumption pathways on a light duty DOC and SCRoF aftertreatment system. This aftertreatment system was aged utilizing an engine test bench modified to enable increased oil consumption rates via three unique oil consumption pathways. The components were characterized for functional SCR performance, ash morphology, and ash deposition characteristics. This included utilizing techniques, such as SEM / EDS, to evaluate the ash structures and quantify the ash elemental composition.

A computational fluid dynamics (CFD) model was developed to explore gasoline compression ignition (GCI) combustion. Results were validated with single-cylinder engine (SCE) experiments. It was shown that the CFD model captured experimental results well. Cylinder pressure, heat release and emissions from the CFD model were also used to analyze the performance of GCI combustion with a current heavy-duty diesel engine platform. This work also provides detailed analysis on in-cylinder combustion and emissions using CFD. It was found that multiple injection strategy can deliver desirable fuel stratification profile that benefits both engine and emissions performance. A wave contoured piston was compared with a stepped-lip type piston for both GCI and Diesel combustion scenarios on the same engine platform. Stepped-lip pistons offer an opportunity to use multiple injection strategies to overcome high UHC emissions of GCI combustion when compared to wave pistons.

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.

The process of developing, parameterizing, validating, and maintaining models occurs within a wide variety of tools, and requires significant time and resources. To maximize model utilization, models are often shared between various toolsets and experts. Model integration is typically divided into two categories: model exchange and model co-simulation. Of these two categories, model co-simulation is typically regarded as the more complex and difficult to implement. Co-Simulation provides the ability to integrate models between different toolsets or incompatible versions of the same software. Additionally, it provides the capabilities for real-time simulations and hardware-in-the-loop test scenarios. This paper reviews some of the common co-simulation data communication methods including pipes and file input/output. The differences between serial and parallel, aka synchronous and asynchronous, communication patterns are also discussed.

Present day natural gas engines have a significant efficiency disadvantage but benefit with low carbon-dioxide emissions and cheap three-way catalysis aftertreatment. The aim of this work is to improve the efficiency of a natural gas engine on par with a diesel engine. A Cummins-Westport ISX12-G (diesel) engine is used for the study. A baseline model is validated in three-dimensional Computational Fluid Dynamics (CFD). The challenge of this project is adapting the diesel engine for the natural gas fuel, so that the increased squish area of the diesel engine piston can be used to accomplish faster natural gas burn rates. A further increase efficiency is achieved by switching to D-EGR technology. D-EGR is a concept where one or more cylinders are run with excess fueling and its exhaust stream, containing H2 and CO, is cooled and fed into the intake stream. With D-EGR although there is an in-cylinder presence of a reactive H2-CO reformate, there is also higher levels of dilution.

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.