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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 study assessed a driver’s ability to safely manage Super Cruise lane changes, both driver commanded (Lane Change on Demand, LCoD) and system triggered Automatic Lane Changes (ALC). Data was gathered under naturalistic conditions on public roads in the Washington, D.C. area with 12 drivers each of whom were provided with a Super Cruise equipped study vehicle over a 10-day exposure period. Drivers were shown how to operate Super Cruise (e.g., system displays, how to activate and disengage, etc.) and provided opportunities to initiate and experience commanded lane changes (LCoD), including how to override the system. Overall, drivers experienced 698 attempted Super Cruise lane changes, 510 Automatic and 188 commanded LCoD lane changes with drivers experiencing an average of 43 Automatic lane changes and 16 LCoD lane changes.

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.

With reference to present literature, most OEMs are working on reducing product development time by around ~20%, through seamless integration of digital ecosystem and focusing on dynamic customer needs. The Systems Engineering approach focuses on functions & systems rather than components. In this approach, designers (Computer Aided Design) / analysts (Computer Aided Engineering) need to understand program requirements early to enable seamless integration. This approach also reduces the number of iterative loops between cross functions thereby reducing the development cycle time. In this paper, we have attempted to tackle a common challenge faced by Closures (Liftgate) engineering: meeting slam durability fatigue life while replicating customer normal and abusive closing behavior.

The automotive seat has undergone significant advancements in technology due to changing customer demands, levels of autonomy and vehicle regulations. These advancements have presented both opportunities and challenges in creating a pleasant experience for customers by ensuring optimal seat comfort and a joyful human experience. Seats are always being built to accommodate different percentiles of occupant comfort requirements; original equipment manufacturers come up with various seating adjustment features. However, there is considerable variation among each percentile of occupants in how they utilize these features to achieve a comfortable seating position based on their unique preferences and circumstances. Additionally, there are variations in occupant postures due to the ways people have adapted their driving habits or styles when it comes to the way they sit.

Automotive seat comfort systems provide occupants with a choice to adjust the seat to individual preference, enhancing the customized comfort feel. Seat comfort systems such as massager, lumbar support bladders, seat cushion bolster bladders and seat back bolster bladders are increasingly adopted in automotive seats as customer demand for customizable seats is on the rise. Development of seat comfort systems is mainly driven by Tier 1 suppliers to an automotive original equipment manufacturer (OEM). The Automotive OEM must wait until the final seat prototype is ready with all the seat comfort systems packaged to evaluate the seat comfort performance. Computer Aided Engineering (CAE) Tools like CASIMIR provide detail dummies representing humans with tissues and muscles, allowing occupant seat comfort to be predicted virtually.

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.

A non-intrusive sensing technique to determine start of combustion for mixing-controlled compression-ignition engines was developed based on an accelerometer mounted to the engine block of a 4-cylinder automotive turbo-diesel engine. The sensing approach is based on a physics-based conceptual model for the signal generation process that relates engine block acceleration to the time derivative of heat release rate. The frequency content of the acceleration and pressure signals was analyzed using the magnitude-squared coherence, and a suitable filtering technique for the acceleration signal was selected based on the result. A method to determine start of combustion (SOC) from the acceleration measurements is presented and validated.

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.

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.

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.

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.

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 initial presence and dynamic formation of internal voids in human body models have been subjects of discussion within the human body modeling community. The relevant physics of the human body are described and the importance of capturing this physics for modeling of internal organ interactions is demonstrated. Basic modeling concepts are discussed along with a proposal of simulation setups designed to verify model behavior in terms of volume and pressure between internal organs.

Internal organ injuries of the chest are one of the leading causes of deaths in motor vehicle crashes. The issue of initial presence and dynamic formation of voids around the heart and aorta is addressed to improve kinematics, force interaction and injury risk assessment of these organs of the Global Human Body Model. Steps to fill the voids are presented.

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%.