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The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.

The phenomenon of drop-wall interaction plays a crucial role in a wide range of industrial applications. When liquid droplets come into contact with a high-temperature surface, it can lead to thermal shock due to rapid temperature fluctuations. This abrupt temperature change can generate thermal stress within the solid wall material. If the thermal stress exceeds the material's strength in that specific stress mode, it can result in material failure. Therefore, it is imperative to delve into the evolving temperature patterns on high-temperature surfaces to optimize material durability. This study focuses on investigating drop-wall interactions within the context of engine environments. To achieve this, the Smoothed Particle Hydrodynamics (SPH) method is employed to simulate the impact of fuel droplets on a silicon nitride wall. The goal is to understand the heat transfer mechanisms, thermal penetration depths, and temperature distributions within the heated wall.

Hydrogen exhibits the notable attribute of lacking carbon dioxide emissions when used in internal combustion engines. Nevertheless, hydrogen has a very low energy density per unit volume, along with large emissions of nitrogen oxides and the potential for backfire. Thus, stratified charge combustion (SCC) is used to reduce nitrogen oxides and increase engine efficiency. Although SCC has the capacity to expand the lean limit, the stability of combustion is influenced by the mixture formation time (MFT), which determines the equivalence ratio. Therefore, quantifying the equivalence ratio under different MFT is critical since it determines combustion characteristics. This study investigates the viability of using a Laser Induced Breakdown Spectroscopy (LIBS) for measuring the jet equivalence ratio. Furthermore, study was conducted to analyze the effect of MFT and the double injection parameter, namely the dwell time and split ratio, on the equivalence ratio.

Methanol is one of the most promising fuels for the decarbonization of the off-road and transportation sectors. Although methanol is typically seen as an alternative fuel for spark ignition engines, mixing-controlled compression ignition (MCCI) combustion is typically preferred in most off-road and medium-and heavy-duty applications due to its high reliability, durability and high-efficiency. In this paper, the potential of using ignition enhancers to enable methanol MCCI combustion was investigated. Methanol was blended with 2-ethylhexyl nitrate (EHN) and experiments were performed in a single-cylinder production-like diesel research engine, which has a displacement volume of 0.83 L and compression ratio of 16:1. The effect of EHN has been evaluated with three different levels (3%vol, 5%vol, and 7%vol) under low- and part-load conditions. The injection timing has been swept to find the stable injection window for each EHN level and load.

Optimizing fuel injection spray is essential to comply with stringent future emission regulations for hybrid vehicles and internal combustion engine vehicles, and the spray characteristics and geometry must be robust for various engine operating conditions. This study presents experimental and numerical assessments of spray for lateral-mounted gasoline direct injection (GDI) sprays with different plume arrangements to analyze collapse characteristics, which can significantly deteriorate the geometry and characteristics of fuel sprays. Novel spray characterization methods are applied to analyze complex spray collapse behaviors using LED-based diffusive back-illuminated extinction imaging (DBIEI) and 3D computed tomographic (CT) image reconstruction. High-fidelity computational fluid dynamics (CFD) simulations are performed to analyze the detailed spray characteristics besides experimental characterization.

Direct injection strategies have been successfully used on spark ignited internal combustion engines for improving performance and reducing emissions. Among the different technologies available, outward opening injectors seem to have found their place in renewable applications running on gaseous fuels, including natural gas or hydrogen, as well as in a few specific liquid fuel applications. In order to understand the key operating principles of these devices, their limitations and the resulting sprays, it is necessary to accurately describe the pintle dynamics. The pintle’s relative position with respect to the injector body defines the internal flow geometry and therefore the injection rates and spray characteristics. In this paper both numerical and experimental investigations of the dynamics of an outward opening injector pintle have been carried out.

Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power using a linear relationship.

There is a growing interest in ammonia as a potential carbon-free fuel due to the current trend of decarbonization in ground transportation. Benefits of ammonia as a fuel include its high volumetric energy density, ease of storage and transportation, and mature manufacturing infrastructure. On the other hand, ammonia suffers from a low flame speed, long ignition delay times and NOx formation. In this work, a computational investigation of ammonia and hydrogen blends in a 0-D homogeneous charge compression ignition reactor is conducted using different blends under a range of engine-relevant conditions. Iso-contours of the crank angle corresponding to 50% of total heat release (CA50) are developed to assess the reactivity of the different blends under different engine speeds and equivalence ratios. The results show that ammonia requires a high inlet temperature to achieve a CA50 close to top dead center (TDC).

To cope with regulatory standards, minimizing tailpipe emissions with rapid catalyst light-off during cold-start is critical. This requires catalyst-heating operation with increased exhaust enthalpy, typically by using late post injections for retarded combustion and, therefore, increased exhaust temperature. However, retardability of post injection(s) is constrained by acceptable pollutant emissions such as unburned hydrocarbon (UHC). This study provides further insight into the mechanisms that control the formation of UHC under catalyst-heating operation in a medium-duty diesel engine, and based on the understanding, develops combustion strategies to simultaneously improve exhaust enthalpy and reduce harmful emissions. Experiments were performed with a full boiling-range diesel fuel (cetane number of 45) using an optimized five-injections strategy (2 pilots, 1 main, and 2 posts) as baseline condition.

The pre-prototype development of a simulated rule-based hybrid energy management strategy for a 2019 Chevrolet Blazer RS converted parallel P4 full hybrid is presented. A vehicle simulation model is developed using component bench data and validated using EPA-reported dynamometer fuel economy test data. A combined Willans line model is proposed for the engine and transmission, with hybrid control rules based on efficiency-derived engine power thresholds. Algorithms are proposed for battery state of charge (SOC) management including engine loading and one pedal strategies, with battery SOC maintained within 20% to 80% safe limits and charge balanced behavior achieved. The simulated rule-based hybrid control strategy for the hybrid vehicle has an energy consumption reduction of 20% for the Hot 505, 3.6% for the HwFET, and 12% for the US06 compared to the stock vehicle.

The influence of driver modeling and drive cycle target speed trace modification on vehicle dynamics within energy consumption simulations is studied. EPA dynamometer speed error criteria and the SAE J2951 Drive Quality Evaluation for Chassis Dynamometer Testing standard are applied to simulation outputs as proposed components of simulation validation, providing guidelines for acceptable vehicle speed outputs and allowing comparison of simulation results to reported EPA dynamometer test statistics. The combined effect of driver model tuning and drive cycle interpolation methods is investigated for the UDDS, HwFET and US06 drive cycles, with EPA-specified linearly interpolated speed trace and a PI controller driver as a baseline result.

Injector performance in gasoline Direct-Injection Spark-Ignition (DISI) engines is a key focus in the automotive industry as the vehicle parc transitions from Port Fuel Injected (PFI) to DISI engine technology. DISI injector deposits, which may impact the fuel delivery process in the engine, sometimes accumulate over longer time periods and greater vehicle mileages than traditional combustion chamber deposits (CCD). These higher mileages and longer timeframes make the evaluation of these deposits in a laboratory setting more challenging due to the extended test durations necessary to achieve representative in-use levels of fouling. The need to generate injector tip deposits for research purposes begs the questions, can an artificial fouling agent to speed deposit accumulation be used, and does this result in deposits similar to those formed naturally by market fuels?

Understanding the fundamental details of drop/wall interactions is important to improving engine performance. Most of the drop-wall interactions studies are based on the impact of a single drop on the wall. To accurately mimic and model the real engine conditions, it is necessary to characterize spray/wall interactions with different impingement frequencies at a wide range of wall temperatures. In this study, a numerical method, based on Smoothed Particle Hydrodynamics (SPH), is used to simulate consecutive droplet impacts on a heated wall both below and above the Leidenfrost temperature. Impact regimes are identified for various impact conditions by analyzing the time evolution of the post-impingement process of n-heptane drops at different impingement frequencies and wall surface temperatures. For wall temperature below the Leidenfrost temperature, the recoiled film does not leave the surface.

A new and unique electric vehicle powertrain model based on bidirectional power flow for propel and regenerative brake power capture is developed and applied to production battery electric vehicles. The model is based on a Willans line model to relate power input from the battery and power output to tractive effort, with one set of parameters (marginal efficiency and an offset loss) for the bidirectional power flow through the powertrain. An electric accessory load is included for the propel, brake and idle phases of vehicle operation. In addition, regenerative brake energy capture is limited with a regen fraction (where the balance goes to friction braking), a power limit, and a low-speed cutoff limit. The purpose of the model is to predict energy consumption and range using only tractive effort based on EPA published road load and test mass (test car list data) and vehicle powertrain parameters derived from EPA reported unadjusted UDDS and HWFET energy consumption.

Pedal misapplication (PM) crashes, i.e., crashes caused by a driver pressing one pedal while intending to press another pedal, have historically been identified by searching unstructured crash narratives for keywords and verified via labor-intensive manual inspection. This study proposes an alternative method to identify PM crashes using event data recorders (EDRs). Since drivers in emergency braking situations are motivated to hit the brake hard, it follows that drivers in emergency braking situations that commit a PM would likewise hit the accelerator hard, likely harder than accelerator pedal application during normal driving. Thus, the time-series accelerator pedal position and the derived accelerator pedal application rate were used to isolate accelerator misapplications. Additional strategic filters were applied based on characteristics observed from previous PM analyses to reduce false positive PM identifications.

With the depletion of petroleum resources around the world, the need to have fuel-efficient mobility solutions and sustainable alternative fuels for automobiles has become prominent. Hybrid Electric Vehicles (HEV) and Battery Electric Vehicles have recently gained attention in terms of fuel-efficient mobility solutions. Recent research work by the authors of these articles and many other research groups have demonstrated the suitability of ammonia as a sustainable alternative power for automobiles [1, 2, 3, 4, 5, 6, 7, 9, 18]. Ammonia has been used for a long period of time mainly as an agricultural chemical and as a sustainable and carbon-free fuel and has substantial potential as a liquid fuel for mobile applications [1, 2, 3, 4, 5, 6, 7]. Ammonia-rich fuels can be used to run HEVs equipped with an Internal Combustion Engine (ICE) as the primary power source and a battery as the secondary energy source.

A CAE-based optimization method is developed for Lube Oil Consumption (LOC) analysis of the piston-ring system. With accurate thermodynamic boundary conditions from 1D engine combustion simulation, piston motion, dynamics of piston ring, and characteristics of oil consumption are simulated using AVL Piston&Ring. The model is validated by comparing with available test data. Good match is achieved. The model is then applied to a diesel engine. The root cause of excessive LOC of the engine has been identified through CAE. The improved understanding has been applied to optimize the piston and piston ring. Engine dyno test, 1200-hour engine durability test, and 45000-kilometer vehicle test have been conducted to validate the optimized design. The experiment results are in good agreement with CAE predictions, and the oil consumption has been improved over the original design.

Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.

Experimental studies of soot particles showed that the intensity ratio of amorphous and graphite layers measured by Raman spectroscopy correlates to soot oxidation reactivities, which is very important for regeneration of the diesel particulate filters and gasoline particulate filters. This physical mechanism is absent in all soot models. In the present paper, a novel two-layer soot model was proposed that considers the amorphous and graphite layers in the soot particles. The soot model considers soot inception, soot surface growth, soot oxidation by O2 and OH, and soot coagulation. It is assumed that amorphous-type soot forms from fullerene. No soot coagulation is considered in the model between the amorphous- and graphitic-types of soot. Benzene is taken as the soot precursor, which is formed from acetylene. The model was implemented into a commercial CFD software CONVERGE using user defined functions. A diesel engine case was simulated.

Machine learning methods, such as decision trees and deep neural networks, are becoming increasingly important and useful for data analysis in various scientific fields including dynamics and control, signal processing, pattern recognition, fluid mechanics, and chemical synthesis, etc. For future engine design and performance optimization, there is an urgent need for a robust predictive model which could capture the major combustion properties such as autoignition and flame propagation of multicomponent fuels under a wide range of engine operating conditions, without massive experimental measurement or computational efforts. It will be shown that these long-held limitations and challenges related to complex fuel combustion and engine research could be readily solved by implementing machine learning methods.