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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.

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.

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.

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.

There is significant potential for connected and autonomous vehicles to impact vehicle efficiency, fuel economy, and emissions, especially for hybrid-electric vehicles. These improvements could have large-scale impact on oil consumption and air-quality if deployed in large Mobility-as-a-Service or ride-sharing fleets. As part of the US Department of Energy's current Advanced Vehicle Technology Competition (AVCT), EcoCAR: The Mobility Challenge, Mississippi State University’s EcoCAR Team is redesigning and doing the development work necessary to convert a conventional gasoline spark-ignited 2019 Chevy Blazer into a hybrid-electric vehicle with SAE Level 2 autonomy. The target consumer segments for this effort are the Mobility-as-a-Service fleet owners, operators and riders. To accomplish this conversion, the MSU team is implementing a P4 mild hybridization strategy that is expected to result in a 30% increase in fuel economy over the stock Blazer.

The effect of low level ethanol fuel on the power and emissions characteristics was studied in a small, mass produced, carbureted, spark-ignited, Briggs and Stratton Vanguard 19L2 engine. Ethanol has been shown to be an attractive renewable fuel by the automotive industry; having anti-knock properties, potential power benefits, and emissions reduction benefits. With increasing availability and the possible mandates of higher ethanol content in pump gasoline, there is interest in exploring the effect of using higher content ethanol fuels in the small utility engine market. The fuels in this study were prepared by gravimetrically mixing 98.7% ethanol with a balance of 87 octane no-ethanol gasoline in approximately 5% increments from pure gasoline to 25% ethanol. Alcor Petrolab performed fuel analysis on the blended fuels and determined the actual volumetric ethanol content was within 2%.

In this work we studied the effects of piston bowl design on combustion in a small-bore direct-injection diesel engine. Two bowl designs were compared: a conventional, omega-shaped bowl and a stepped-lip piston bowl. Experiments were carried out in the Sandia single-cylinder optical engine facility, with a medium-load, mild-boosted operating condition featuring a pilot+main injection strategy. CFD simulations were carried out with the FRESCO platform featuring full-geometric body-fitted mesh modeling of the engine and were validated against measured in-cylinder performance as well as soot natural luminosity images. Differences in combustion development were studied using the simulation results, and sensitivities to in-cylinder flow field (swirl ratio) and injection rate parameters were also analyzed.

Diesel piston bowl geometry can affect turbulent mixing and therefore it impacts heat-release rates, thermal efficiency, and soot emissions. The focus of this work is on the effects of bowl geometry and injection timing on turbulent flow structure. This computational study compares engine behavior with two pistons representing competing approaches to combustion chamber design: a conventional, re-entrant piston bowl and a stepped-lip piston bowl. Three-dimensional computational fluid dynamics (CFD) simulations are performed for a part-load, conventional diesel combustion operating point with a pilot-main injection strategy under non-combusting conditions. Two injection timings are simulated based on experimental findings: an injection timing for which the stepped-lip piston enables significant efficiency and emissions benefits, and an injection timing with diminished benefits compared to the conventional, re-entrant piston.

We present an approach in which an open-source software infrastructure is used for testing the behavior of autonomous vehicles through computer simulation. This software infrastructure is called CAVE, from Connected Autonomous Vehicle Emulator. As a software platform that allows rapid, low-cost and risk-free testing of novel designs, methods and software components, CAVE accelerates and democratizes research and development activities in the field of autonomous navigation.

We present an approach in which we use simulation to capture the two-way coupling between the dynamics of a vehicle and that of a fluid that sloshes in a tank attached to the vehicle. The simulation is carried out in and builds on support provided by two modules: Chrono::FSI (Fluid-Solid Interaction) and Chrono::Vehicle. The dynamics of the fluid phase is governed by the mass and momentum (Navier-Stokes) equations, which are discretized in space via a Lagrangian approach called Smoothed Particle Hydrodynamics. The vehicle dynamics is the solution of a set of differential algebraic equations of motion. All equations are discretized in time via a half-implicit symplectic Euler method. This solution approach is general - it allows for fully three dimensional (3D) motion and nonlinear transients. We demonstrate the solution in conjunction with the simulation of a vehicle model that performs a constant radius turn and double lane change maneuver.

In this work, linear, non-linear and a generalized renormalization group (RNG) two-equation RANS turbulence models of the k-epsilon form were compared for the prediction of turbulent compressible flows in diesel engines. The object-oriented, multidimensional parallel code FRESCO, developed at the University of Wisconsin, was used to test the alternative models versus the standard k-epsilon model. Test cases featured the academic backward facing step and the impinging gas jet in a quiescent chamber. Diesel engine flows featured high-pressure spray injection in a constant volume vessel from the Engine Combustion Network (ECN), as well as intake flows in a high-swirl diesel engine. For the engine intake flows, a model of the Sandia National Laboratories 1.9L light-duty single cylinder optical engine was used.

Past research has shown that reactivity controlled compression ignition (RCCI) combustion offers efficiency and NOx and soot advantages over conventional diesel combustion at mid load conditions. However, at high load and low speed conditions, the chemistry timescale of the fuel shortens and the engine timescale lengthens. This mismatch in timescales makes operation at high load and low speed conditions difficult. High levels of exhaust gas recirculation (EGR) can be used to extend the chemistry timescales; however, this comes at the penalty of increased pumping losses. In the present study, targeting the high load - low speed regime, computational optimizations of RCCI combustion were performed at 20 bar gross indicated mean effective pressure (IMEP) and 1300 rev/min. The two fuels used for the study were gasoline (low reactivity) and diesel (high reactivity).

Due to concerns regarding pollutant and CO2 emissions, advanced combustion modes that can simultaneously reduce exhaust emissions and improve thermal efficiency have been widely investigated. The main characteristic of the new combustion strategies, such as HCCI and LTC, is that the formation of a homogenous mixture or a controllable stratified mixture is required prior to ignition. The major issue with these approaches is the lack of a direct method for the control of ignition timing and combustion rate, which can be only indirectly controlled using high EGR rates and/or lean mixtures. Homogeneous Charge Progressive Combustion (HCPC) is based on the split-cycle principle. Intake and compression phases are performed in a reciprocating external compressor, which drives the air into the combustor cylinder during the combustion process, through a transfer duct. A transfer valve is positioned between the compressor cylinder and the transfer duct.

Reactivity-controlled compression ignition (RCCI) has been shown to be capable of providing improved engine efficiencies coupled with the benefit of low emissions via in-cylinder fuel blending. Much of the previous body of work has studied the benefits of RCCI operation using high injection pressures (e.g., 500 bar or greater) with common rail injection (CRI) hardware. However, low-pressure fueling technology is capable of providing significant cost savings. Due to the broad market adoption of gasoline direct injection (GDI) fueling systems, a market-type prototype GDI injector was selected for this study. Single-cylinder light-duty engine experiments were undertaken to examine the performance and emissions characteristics of the RCCI combustion strategy with low-pressure GDI technology and compared against high injection pressure RCCI operation. Gasoline and diesel were used as the low-reactivity and high-reactivity fuels, respectively.

In-cylinder temperature and velocity fields were quantified simultaneously in an optically accessible, small-bore diesel engine. A technique utilizing luminescence from Pr:YAG phosphor particles aerosolized into the intake air was used for temperature determination while particle image velocimetry (PIV) on the aforementioned phosphor particles was used to simultaneously measure the velocity field. The temperature and velocity fields were measured at different points throughout the compression stroke up to −30 CAD. Systematic interference due to emission from the piston window reduced the accuracy of the measurements at crank angles closer to TDC. Single-shot simultaneous measurements of the temperature and velocity fields were made using both unheated and heated intake temperatures. In both cases, cycle-to-cycle variations in the temperature and velocity fields were visible.

A Three-Way Catalyst (TWC) model with global TWC kinetics for lean burn DISI engines were developed and validated. The model incorporates kinetics of hydrocarbons and carbon monoxide oxidations, NOx reduction, water-gas and steam reforming and oxygen storage. Ammonia (NH₃) and new nitrous oxide (N₂O) kinetics were added into the model to study NH₃ and N₂O formation in TWC systems. The model was validated over a wide range of engine operating conditions using various types of experimental data from a lean burn automotive SIDI engine. First, well-controlled time-resolved steady state data were used for calibration and initial model tests. In these steady state operations, the engine was switched between lean and rich conditions for NOx emission control. Then, the model was further validated using a large set of time-averaged steady state data. Temperature dependencies of NH₃ and N₂O kinetics in the TWC model were examined and well captured by the model.

A fundamentally based quasi-dimensional NOx emission model for spark ignition direct injection (SIDI) gasoline engines was developed. The NOx model consists of a chemical mechanism and three sub-models. The classical extended Zeldovich mechanism and N₂O pathway for NOx formation mechanism were employed as the chemical mechanism in the model. A characteristic time model for the radical species H, O and OH was incorporated to account for non-equilibrium of radical species during combustion. A model of homogeneity which correlates fundamental dimensionless numbers and mixing time was developed to model the air-fuel mixing and inhomogeneity of the charge. Since temperature has a dominant effect on NOx emission, a flame temperature correlation was developed to model the flame temperature during the combustion for NOx calculation. Measured NOx emission data from a single-cylinder SIDI research engine at different operating conditions was used to validate the NOx model.