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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.

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.

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.

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.

Engine braking is a supplemental retarding technology in addition to foundational friction brakes in commercial vehicles. This technology is in use in Europe & Americas for several decades now. In engine braking, the engine acts as a compressor, thus producing the required braking power. The braking power is generated by either reducing the volumetric efficiency or increasing the pressure difference across the cylinder. This is usually achieved by means of exhaust valve lift modulation. There are dominantly two types of engine brakes viz. bleeder brake and compression release brake. The present work uses GT-Power® model to study the braking performance of a 4-cylinder, medium duty diesel engine at different engine RPMs and valve lifts. The work brings out a comprehensive understanding of different lift events and their effects on braking performance.

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.

Compared to the internal-combustion-engine (ICE) vehicles on the road today, Electric Vehicles (EV) deliver more torque to vehicle wheels, and require smaller driveline packaging envelopes. Current differentials use asymmetrical ring gears with differential housings that are roughly a third of the tire outside diameter. New differential architecture concepts are shown here to deliver more torque to the wheels, while decreasing the height of the differential as much as fourfold. Most EV’s are driven by one or more torsion motors, delivering torque to the left side and the right side of the EV’s at different speeds during a vehicle turn, or a wheel “spinout.” At low speeds, the EV motors deliver more torque to the wheels than comparably sized ICE vehicles, so EV differentials must be built stronger and stiffer to manage the distribution of available drive torque.

Functionally graded materials enable structures to have distribution of different properties (physical, thermal, electrical, mechanical, etc.) across its volume; achievable via material/ design/ process engineering. These functionally graded materials can find an application in systems which demand localized variation or enhancement in properties in different regions of the same component. In this paper, we focus on the potential ways of designing functionally graded polymer composite structure by injection molding process. Advanced mold designs for injection molding process can be effectively used to manufacture the functionally graded structures. Innovative design approach has been explored to control the distribution of the filler content /orientation to impart distinctive properties across the cross section / geometry without affecting the bulk properties.

The effect of equivalence ratio on the particulate size distribution (PSD) in a spark-ignited, direct-injected (SIDI) engine was investigated. A single-cylinder, four-stroke, spark-ignited direct-injection engine fueled with certification gasoline was used for the measurements. The engine was operated with early injection during the intake stroke. Equivalence ratio was swept over the range where stable combustion was achieved. Throughout this range combustion phasing was held constant. Particle size distributions were measured as a function of equivalence ratio. The data show the sensitivity of both engine-out particle number and particle size to global equivalence ratio. As equivalence ratio was increased a larger fraction of particles were due to agglomerates with diameters ≻ 100 nm. For decreasing equivalence ratio smaller particles dominate the distribution. The total particle number and mass increased non-linearly with increasing equivalence ratio.

A simulation framework with a validated system model capable of estimating fuel consumption is a valuable tool in analysis and design of the hybrid vehicles. In particular, the framework can be used for (1) benchmarking the fuel economy achievable from alternate hybrid powertrain technologies, (2) investigating sensitivity of fuel savings with respect to design parameters (for example, component sizing), and (3) evaluating the performance of various supervisory control algorithms for energy management. This paper describes such a simulation framework that can be used to predict fuel economy of series hydraulic hybrid vehicle for any specified driver demand schedule (drive cycle), developed in MATLAB/Simulink. The key components of the series hydraulic hybrid vehicle are modeled using a combination of first principles and empirical data. A simplified driver model is included to follow the specified drive cycle.

This paper discusses the effect of torque vectoring differential on improving vehicle handling and stability performance. The torque vectoring concept has been analyzed. The vehicle discussed in this paper is an AWD vehicle with torque vectoring differential in the rear and a torque biasing center differential. First, simulation results with vehicle model in CarSim® and torque vectoring control algorithm in Matlab®/Simulink® is discussed. Then, experimental results for vehicle tested at winter and summer test facility is presented. Both simulation and experimental results demonstrate the effectiveness of torque vectoring differential on vehicle handling & stability.

This paper addresses Hardware-In-the-Loop modeling and simulation for Diesel aftertreatment controls system development. Lean NOx Trap (LNT) based aftertreatment system is an efficient way to reduce NOx emission from diesel engines. From control system perspective, the main challenge in aftertreatment system is to predict temperature at various locations and estimate the stored NOx in LNT. Accurate estimation of temperatures and NOx stored in the LNT will result in an efficient system control with less fuel penalty while still maintaining the emission requirements. The optimization of the controls will prolong the lifespan of the system by avoiding overheating the catalysts, and slow the progressive process of component aging. Under real world conditions, it is quite difficult and costly to test the performance of a such complex controller by using only vehicle tests and engine cells.

This article aims to show how the development of innovative products within the automotive industry in Brazil has been oriented, linking technological competences construction in local poles with suppliers and headquarters cooperation. The discussion in this article is illustrated by the development and application analysis of an electronic locker differential integrated to a front transversal transmission, which is the world's first commercial application. It proposes, through a case study, a relationship between the subjects discussed in here and the new tendencies for product development within the automotive industry and also for the world's R&D flow.

Optimal fuel injection strategies are obtained with a micro-genetic algorithm and an adaptive gradient method for a nonroad, medium-speed DI diesel engine equipped with a multi-orifice, asynchronous fuel injection system. The gradient optimization utilizes a fast-converging backtracking algorithm and an adaptive cost function which is based on the penalty method, where the penalty coefficient is increased after every line search. The micro-genetic algorithm uses parameter combinations of the best two individuals in each generation until a local convergence is achieved, and then generates a random population to continue the global search. The optimizations have been performed for a two pulse fuel injection strategy where the optimization parameters are the injection timings and the nozzle orifice diameters.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

The fuel film thickness resulting from diesel fuel spray impingement was measured in a chamber at conditions representative of early injection timings used for low temperature diesel combustion. The adhered fuel volume and the radial distribution of the film thickness are presented. Fuel was injected normal to the impingement surface at ambient temperatures of 353 K, 426 K and 500 K, with densities of 10 kg/m3 and 25 kg/m3. Two injectors, with nozzle diameters of 100 μm and 120 μm, were investigated. The results show that the fuel film volume was strongly affected by the ambient temperature, but was minimally affected by the ambient density. The peak fuel film thickness and the film radius were found to increase with decreased temperature. The fuel film was found to be circular in shape, with an inner region of nearly constant thickness. The major difference observed with temperature was a decrease in the radial extent of the film.

Farm workers experience a very high incidence of injuries leading to physical and cognitive (strokes, TBI) disabilities. Since 1991, the AgrAbility Project 2 and its staff have provided direct assistance and education to many U.S. farmers and farm workers. If farmers, ranchers or farm workers who become disabled continue to be employed in agriculture, often their agricultural operation must be modified and/or agricultural machinery must be modified or adaptive equipment purchased to meet their new needs. Some common tractor modifications include operator lifts, hand controls, added/modified steps and handrails, automated hitches, and custom seating. Some modifications are commercially available but others are done on an individual need basis. AgrAbility staff would welcome the opportunity to work closer with farm equipment manufacturers to create modifications that would make farming and ranching easier and safer for all.

Large-scale flows in internal combustion engines directly affect combustion duration and emissions production. These benefits are significant given increasingly stringent emissions and fuel economy requirements. Recent efforts by engine manufacturers to improve in-cylinder flows have focused on the design of specially shaped intake ports. Utility engine manufacturers are limited to simple intake port geometries to reduce the complexity of casting and cost of manufacturing. These constraints create unique flow physics in the engine cylinder in comparison to automotive engines. An experimental study of intake-generated flows was conducted in a four-stroke spark-ignition utility engine. Steady flow and in-cylinder flow measurements were made using three simple intake port geometries at three port orientations. Steady flow measurements were performed to characterize the swirl and tumble-generating capability of the intake ports.

The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and is based on the dynamic programming technique. A vehicle model is first developed, and the optimal control actions to maximize fuel economy are then obtained by the dynamic programming method. A near-optimal control strategy is subsequently extracted and implemented using a rapid-prototyping control development system, which provides a convenient environment to adjust the control algorithms and accommodate various I/O configurations. Dynamometer-testing results confirm that the proposed algorithm helps the prototype hybrid truck to achieve a 45% fuel economy improvement on the benchmark (non-hybrid) vehicle. It also compares favorably to a conventional rule-based control method, which only achieves a 31% fuel economy improvement on the same hybrid vehicle.