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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.

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.

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.

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.

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.

As the efforts to push capabilities of current engine hardware to their durability limits increases, more accurate and reliable analysis is necessary to ensure that designs are robust. This paper evaluates a method of Conjugate Heat Transfer (CHT) analysis for a gasoline and a diesel engine that combines combustion Computational Fluid Dynamics (CFD), engine Finite Element Analysis (FEA), and cooling jacket CFD with the goal of obtaining more accurate temperature distribution and heat loss predictions in an engine compared to standard de-coupled CFD and FEA analysis methods. This novel CHT technique was successfully applied to a 2.5 liter GM LHU gasoline engine at 3000 rpm and a 15.0 liter Cummins ISX heavy duty diesel engine operating at 1250 rpm. Combustion CFD simulations results for the gasoline and diesel engines are validated with the experimental data for cylinder pressure and heat release rate.

Diesel engines are facing increased competition from gasoline engines in the light-duty and small non-road segments, primarily due to the high relative cost of emissions control systems for lean-burn diesel engines. Advancements in gasoline engine technology have decreased the operating cost advantage of diesels and the relatively high initial-cost disadvantage is now too large to sustain a strong business position. SwRI has focused several years of research efforts toward enabling diesel engine combustion systems to operate at stoichiometric conditions, which allows the application of a low-cost three-way catalyst emission control system which has been well developed for gasoline spark-ignited engines. One of the main barriers of this combustion concept is the result of high smoke emissions from poor fuel/air mixing.

As the design of engines advances and continues to push the capabilities of current hardware closer to their durability limits, more accurate and reliable analysis is necessary to ensure that designs are robust. This research evaluates a method of conjugate heat transfer analysis for a diesel engine that combines the combustion CFD, Engine FEA, and cooling jacket CFD with the aim of getting more accurate heat loss predictions and a more accurate temperature distribution in the engine than with current analysis methods. A 15.0 L Cummins ISX heavy duty engine operating at 1250 RPM and 15 bar BMEP load is selected for this work. Spray combustion computational fluid dynamics (CFD) simulations are performed for the diesel engine and the results are validated with experimental data. Finite Element Analysis (FEA) simulations were performed in a separate software platform.

Numerous studies have characterized the impact of high injection pressure and small nozzle holes on spray quality and the subsequent impact on combustion. Higher injection pressure or smaller nozzle diameter usually reduce soot emissions owing to better atomization quality and fuel-air mixing enhancement. The influence of nozzle geometry on spray and combustion of diesel continues to be a topic of great research interest. An alternate approach impacting spray quality is investigated in this paper, specifically the impact of non-circular nozzles. The concept was explored experimentally in an optically accessible constant-volume combustion chamber (CVCC). Non-reacting spray evaluations were conducted at various ambient densities (14.8, 22.8, 30 kg/m3) under inert gas of Nitrogen (N2) while injection pressure was kept at 100 MPa. Shadowgraph imaging was used to obtain macroscopic spray characteristics such as spray structure, spray penetration, and the spray cone angle.

Fluid-Structure Interaction (FSI) simulation approach can be used to simulate a turbocharger. However, this predictive 3D simulation encounters the challenge of a long computational time. The impeller speed can be above 100,000 rpm, and generally a CFD solver limits the maximum movement of the impeller surface per time step. The maximum movement must be a fraction (~0.3) of the cell length, thus the time step will be very small. A Multiple Reference Frame (MRF) approach can reduce computational time by eliminating the need to regenerate the mesh at each time-step to accommodate the moving geometry. A static local reference zone encompassing the impeller is created and the impact of the impeller movement is modeled via a momentum source. However, the MRF approach is not a predictive simulation because the impeller speed must be given by the User. A new simulation approach was introduced that coupled the FSI and MRF approach.