Search Results

The SAE AutoDrive Challenge II is a four-year collegiate competition dedicated to developing a Level 4 autonomous vehicle by 2025. In January 2023, the participating teams each received a Chevy Bolt EUV. Within a span of five months, the second phase of the competition took place in Ann Arbor, MI. The authors of this contribution, who participated in this event as team Wisconsin Autonomous representing the University of Wisconsin–Madison, secured second place in static events and third place in dynamic events. This has been accomplished by reducing reliance on the actual vehicle platform and instead leveraging physical analogs and simulation. This paper outlines the software and hardware infrastructure of the competing vehicle, touching on issues pertaining sensors, hardware, and the software architecture employed on the autonomous vehicle. We discuss the LiDAR-camera fusion approach for object detection and the three-tier route planning and following systems.

The influence of a split-injection strategy on energy-assisted compression-ignition (EACI) combustion of low-cetane number sustainable aviation fuels was investigated in a single-cylinder direct-injection compression-ignition engine using a ceramic ignition assistant (IA). Two low-cetane number fuels were studied: a low-cetane number alcohol-to-jet (ATJ) sustainable aviation fuel (SAF) with a derived cetane number (DCN) of 17.4 and a binary blend of ATJ with F24 (Jet-A fuel with military additives, DCN 45.8) with a blend DCN of 25.9 (25 vol.% F24, 75 vol.% ATJ). A pilot injection mass sweep (3.5-7.0 mg) with constant total injection mass and an injection dwell sweep (1.5-3.0 ms) with fixed main injection timing was performed. Increasing pilot injection mass was found to reduce cycle-to-cycle combustion phasing variability by promoting a shorter and more repeatable combustion event for the main injection with a shorter ignition delay.

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.

Ensuring spray robustness of gasoline direct injection (GDI) is essential to comply with stringent future emission regulations for hybrid and internal combustion engine vehicles. This study presents experimental and numerical assessments of spray for lateral-mounted GDI sprays with two different plume arrangements to analyze spray collapse characteristics, which can significantly deteriorate the atomization performance of fuel sprays. Novel spray characterization methods are applied to analyze complex spray collapse behaviors using diffusive back-illuminated extinction imaging (DBIEI) and 3D computed tomographic (CT) image reconstruction. A series of computational fluid dynamics (CFD) simulations are performed to analyze the detailed spray characteristics besides experimental characterization. Spatio-temporal plume dynamics of conventional triangle-pattern spray are evaluated and compared to a plume pattern with an inversed T pattern that has more open space between plumes.

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.

This work experimentally investigates the impact of premixed fuel composition (methane/ethane, methane/propane, and methane/hydrogen mixtures having equivalent chemical energy) and pilot reactivity (cetane number) on diesel-pilot injection (DPI) combustion performance and emissions, with an emphasis on the pilot ignition delay (ID). To support the experimental pilot ignition delay trends, an analysis technique known as Mixing Line Concept (MLC) was adopted, where the cold diesel surrogate and hot premixed charge are envisioned to mix in a 0-D constant volume reactor to account for DPI mixture stratification. The results show that the dominant effect on pilot ignition is the pilot fuel cetane number, and that the premixed fuel composition plays a minor role. There is some indication of a physical effect on ignition for cases containing premixed hydrogen.

Lithium-ion and Lithium polymer batteries are fast becoming ubiquitous in high-discharge rate applications for military and non-military systems. Applications such as small aerial vehicles and energy transfer systems can often function at C-rates greater than 1. To maximize system endurance and battery health, there is a need for models capable of precisely estimating the battery state-of-charge (SoC) under all temperature and loading conditions. However, the ability to perform state estimation consistently and accurately to within 1% error has remained unsolved. Doing so can offer enhanced endurance, safety, reliability, and planning, and additionally, simplify energy management. Therefore, the work presented in this paper aims to study and develop experimentally validated mathematical models capable of high-accuracy battery SoC estimation.

In this study, the impacts of fuel volatility and reactivity on combustion stability and emissions were studied in a light-duty single-cylinder research engine for a three-injection catalyst heating operation strategy with late post-injections. N-heptane and blends of farnesane/2,2,4,4,6,8,8-heptamethylnonane were used to study the impacts of volatility and reactivity. The effect of increased chemical reactivity was also analysed by comparing the baseline #2 diesel operation with a pure blend of mono-ether components (CN > 100) representative of potential high cetane oxygenated bioblendstocks and a 25 vol.% blend of the mono-ether blend and #2 diesel with a cetane number (CN) of 55. At constant reactivity, little to no variation in combustion performance was observed due to differences in volatility, whereas increased reactivity improved combustion stability and efficiency at late injection timings.

The combined impacts of engine speed and fuel reactivity on energy-assisted compression-ignition (EACI) combustion using a commercial off-the-shelf (COTS) ceramic glow plug for low-load operation werexxz investigated. The COTS glow plug, used as the ignition assistant (IA), was overdriven beyond its conventional operation range. Engine speed was varied from 1200 RPM to 2100 RPM. Three fuel blends consisting of a jet-A fuel with military additives (F24) and a low cetane number alcohol-to-jet (ATJ) sustainable aviation fuel (SAF) were tested with cetane numbers (CN) of 25.9, 35.5, and 48.5. The ranges of engine speed and fuel cetane numbers studied are significantly larger than those in previous studies of EACI or glow-plug assisted combustion, and the simultaneous variation of engine speed and fuel reactivity are unique to this work. For each speed and fuel, a single-injection of fixed mass was used and the start of injection (SOI) was swept for each IA power.

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 latest edition of the US Department of Energy's (DOE) Advanced Vehicle Technology Competition (AVTC) series is the EcoCAR Mobility Challenge (EMC). In the third year of the EMC, the Mississippi State University (MSU) team developed and tested a perception system and a longitudinal controller to achieve SAE level 2 autonomy. Our team leveraged the model-based design approach to iterate between developing software components and executing tests in multiple environments in the loop (XIL) to verify that design requirements are met. This workflow allowed us to detect and resolve issues early in the development process. The perception system is composed of a sensor fusion and tracking algorithm. It relies on detections from a front facing camera and radar to generate tracks for a leading vehicle. The tracks from the perception system are used by a model predictive controller (MPC) to maintain a safe distance to the leading vehicle.

Identifying edge cases for designed algorithms is critical for functional safety in autonomous driving deployment. In order to find the feasible boundary of designed algorithms, simulations are heavily used. However, simulations for autonomous driving validation are expensive due to the requirement of visual rendering, physical simulation, and AI agents. In this case, common sampling techniques, such as Monte Carlo Sampling, become computationally expensive due to their sample inefficiency. To improve sample efficiency and minimize the number of simulations, we propose a tailored active learning approach combining the Support Vector Machine (SVM) and the Gaussian Process Regressor (GPR). The SVM learns the feasible boundary iteratively with a new sampling point via active learning. Active Learning is achieved by using the information of the decision boundary of the current SVM and the uncertainty metric calculated by the GPR.

The EcoCAR Mobility Challenge (EMC) is the latest edition of the Advanced Vehicle Technology Competition (AVTC) series sponsored by the US Department of Energy. This competition challenges 11 North American universities to redesign a stock 2019 Chevrolet Blazer into an energy-efficient, SAE level 2-autonomous mild hybrid electric vehicle (mHEV) for use in the Mobility as a Service (MaaS) market. The Mississippi State University (MSU) team designed a P4 electric powertrain with an 85kW (113.99 HP) permanent magnet synchronous machine (PMSM) powered by a custom 5.4 kWh lithium-ion energy storage system. To maximize energy efficiency, Model Based Design concepts were leveraged to optimize the overall gear ratio for the P4 system. To accommodate this optimized ratio in the stock vehicle, a custom offset gearbox was designed that links the PMSM to the rear drive module.

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios.

The fuel spray process is of utmost importance to internal combustion engine design as it dominates engine performance and emissions characteristics. While designers rely on computational fluid dynamics (CFD) modeling for understanding of the air-fuel mixing process, there are recognized shortcomings in current CFD spray predictions, particularly under super-critical or flash-boiling conditions. In contrast, time-resolved optical spray experiments have now produced datasets for the three-dimensional liquid distribution for a wide range of operating conditions and fuels. By utilizing such a large amount of detailed experimental data, the machine learning (ML) techniques have opened new pathways for the prediction of fuel sprays under various engine-like conditions.

Traffic state identification is a key problem in intelligent transportation system. As a new technology, connected and automated vehicle can play a role of identifying traffic state with the installation of onboard sensors. However, research of lane level traffic state identification is relatively lacked. Identifying lane level traffic state is helpful to lane selection in the process of driving and trajectory planning. In addition, traffic state identification precision with low penetration of connected and automated vehicles is relatively low. To fill this gap, this paper proposes a novel method of identifying traffic state in the presence of connected and automated vehicles with low penetration rate. Assuming connected and automated vehicles can obtain information of surrounding vehicles’, we use the perceptible information to estimate imperceptible information, then traffic state of road section can be inferred.

In-cylinder thermal barrier coatings (TBCs) have the capability to reduce fuel consumption by reducing wall heat transfer and to increase exhaust enthalpy. Low thermal conductivity, low volumetric heat capacity thermal barrier coatings tend to reduce the gas-wall temperature difference, the driving potential for heat transfer from the gas to the combustion chamber surfaces. This paper presents a coupling between an analytical methodology for multi-layer coated wall surface temperature prediction with a fully calibrated production model in a commercial system-level simulation software package (GT-Power). The wall surface temperature at each time step was calculated efficiently by convolving the engine wall response function with the time-varying surface boundary condition, i. e., in-cylinder heat flux and coolant temperature. This tool allows the wall to be treated either as spatially uniform with one set of properties, or with independent head/piston/liner components.

The autonomous collision avoidance problem for multi-trailer vehicle maneuvering is investigated in this paper. Different from conventional vehicle systems that contain one single moving part or multi-parts that can be considered as one rigid body, the interconnection between the tractor and each trailer, and interactions between trailers in the multi-trailer system introduce a high dimensional and highly complex dynamic system for the controller design. The external disturbance and parametric uncertainties further increase the difficulty in system identification and state space formulation. To implement a real time control system for various scenarios where the locations and states of the obstacles are not known beforehand, a supervisory algorithm is designed to convert the control problem to a discrete event system. The model predictive control (MPC) using limited lookahead policy is employed in the proposed algorithm.

Appropriate spray modeling in multidimensional simulations of diesel engines is well known to affect the overall accuracy of the results. More and more accurate models are being developed to deal with drop dynamics, breakup, collisions, and vaporization/multiphase processes; the latter ones being the most computationally demanding. In fact, in parallel calculations, the droplets occupy a physical region of the in-cylinder domain, which is generally very different than the topology-driven finite-volume mesh decomposition. This makes the CPU decomposition of the spray cloud severely uneven when many CPUs are employed, yielding poor parallel performance of the spray computation. Furthermore, mesh-independent models such as collision calculations require checking of each possible droplet pair, which leads to a practically intractable O(np2/2) computational cost, np being the total number of droplets in the spray cloud, and additional overhead for parallel communications.

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.