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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.

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.

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.

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.

High-resolution planar laser-induced fluorescence (PLIF) measurements were performed on the scalar field in an optical engine. The measurements were of sufficient resolution to fully resolve all of the length scales of the flow field through the full cycle. The scalar dissipation spectrum was calculated, and by fitting the results to a model turbulent spectrum the Batchelor scale of the turbulent flow was estimated. The scalar inhomogeneity was introduced by a low-momentum gas jet injection. A consistent trend was observed in all data; the Batchelor scale showed a minimum value at top dead center (TDC) and was nearly symmetric about TDC. Increasing the engine speed resulted in a decrease of the Batchelor scale, and the presence of a shroud on the intake valve, which increased the turbulence intensity, also reduced the Batchelor scale. The effect of the shrouded valve was less significant compared to the effect of engine speed.

This paper presents advanced and cost-reducing technologies of a motor drive system with reduced permanent magnets but without a position sensor. The key enabler is the integration of novel designs of flux-intensifying interior permanent magnet synchronous machines (FI-IPMSMs) and position self-sensing control technologies. In this paper, we focus on two advantages of FI-IPMSM over conventional flux-weakening interior permanent magnet synchronous machines (FW-IPMSMs). The first benefit is that thinner magnets are possible and there is less concern for demagnetization because of its significantly smaller flux-weakening current. This paper presents two design examples of FI-IPMSMs, one of which has not only smaller magnets but also similar power conversion capability. The second advantage is reduced saturation and cross-saturation effect, which leads to improved position self-sensing capability.

Permanent magnet AC generators are robust, inexpensive, and efficient compared to wound-field synchronous generators with brushless exciters. Their application in variable-speed applications is made difficult by the variation of the stator voltage with shaft speed. This paper presents the use of stator-side reactive power injection as a means of regulating the stator voltage. Design-oriented analysis of machine performance for this mode of operation identifies an appropriate level of machine saliency that enables excellent terminal voltage regulation over a specified speed and load range, while minimizing stator current requirements. This paper demonstrates that the incorporation of saliency into the permanent magnet generator can significantly reduce the size of the reactive current source that is required to regulate the stator voltage during operation over a wide range of speeds and loads.

The University of Wisconsin - Madison Clean Snowmobile team has designed, constructed and now refined an electric snowmobile with 40 km (24 mi) range and acceleration comparable to a 75 kW (100 hp) internal-combustion-powered snowmobile. Starting with a Polaris IQ Fusion chassis, a direct-drive chain-case was engineered to couple a General Motors EV1 copper-bar rotor AC induction electric motor to the track drive shaft. The battery pack uses 104 28 V, 2.8 A-hr Lithium-Ion battery modules supplied by Milwaukee Tool to store 8.2 kW-hr of energy at a nominal voltage of 364 V. Power is transmitted to the electric motor via an Azure Dynamics DMOC445LLC motor controller. All of the components fit within the original sled envelope, leading to a vehicle with conventional appearance and a total mass of 313 kg (690 lb). The vehicle, dubbed the BuckEV, accelerates to 150 m (500 ft) in 6.9 seconds and has a top speed of 122 km/hr (76 mph) with a pass-by sound level of 55 dB.

The University of Wisconsin Hybrid Vehicle Team has implemented and optimized a four-wheel drive, charge sustaining, split-parallel hybrid-electric crossover vehicle for entry into the 2008 ChallengeX competition. This four year project is based on a 2005 Chevrolet Equinox platform. Fuel economy, greenhouse gas impact (GHGI), acceleration, component packaging and consumer acceptability were appropriately weighted to determine powertrain component selections. Wisconsin's Equinox, nicknamed the Moovada, is a split-parallel hybrid utilizing a General Motors (GM) 110 kW 1.9L CDTi (common rail diesel turbo injection) engine coupled to an F40 6-speed manual transmission. The rear axle is powered by a SiemensVDO induction motor/gearbox power-limited to 65 kW by a 40-module (288 volts nominal) Johnson Controls Inc, nickel-metal hydride battery pack.

This paper investigates flow and combustion in a full-cycle simulation of a four-stroke, three-valve HCCI engine by visualizing the flow with pathlines. Pathlines trace massless particles in a transient flow field. In addition to visualization, pathlines are used here to trace the history, or evolution, of flow fields and species. In this study evolution is followed from the intake port through combustion. Pathline analysis follows packets of intake charge in time and space from induction through combustion. The local scalar fields traversed by the individual packets in terms of velocity magnitude, turbulence, species concentration and temperatures are extracted from the simulation results. The results show how the intake event establishes local chemical and thermal environments in-cylinder and how the species respond (chemically react) to the local field.

Humidity control within confined spaces is of great importance for existing NASA environmental control systems and Exploration applications. The Engineered Multifunction Surfaces (MFS) developed in this STTR Phase II form the foundation for a modular and scalable Distributed Humidity Control System (DHCS) while minimizing power, size and mass requirements. Key innovations of the MFS-based DHCS include passive humidity collection, control, and phase separation without moving parts, durable surface properties without particulate generation and accumulation, and the ability to scale up, or network in a distributed manner, a compact, modular device for Exploration applications including space suits, CEV, Rovers, Small and Transit Habitats and Large Habitats.

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 University of Wisconsin - Madison Hybrid Vehicle Team has designed, fabricated, tested and optimized a four-wheel drive, charge sustaining, split-parallel hybrid-electric crossover vehicle for entry into the 2006 Challenge X competition. This multi-year project is based on a 2005 Chevrolet Equinox platform. Trade-offs in fuel economy, greenhouse gas impact (GHGI), acceleration, component packaging and consumer acceptability were weighed to establish Wisconsin's Vehicle Technical Specifications (VTS). Wisconsin's Equinox, nicknamed the Moovada, utilizes a General Motors (GM) 110 kW 1.9 L CIDI engine coupled to GM's 6-speed F40 transmission. The rear axle is powered by a 65 kW Ballard induction motor/gearbox powered from a 44-module (317 volts nominal) Johnson Controls Inc., nickel-metal hydride hybrid battery pack. It includes a newly developed proprietary battery management algorithm which broadcasts the battery's state of charge onto the CAN network.

The University of Wisconsin-Madison Snowmobile Team designed and constructed a hybrid-electric snowmobile for the 2005 Society of Automotive Engineers' Clean Snowmobile Challenge. Built on a 2003 cross-country touring chassis, this machine features a 784 cc fuel-injected four-stroke engine in parallel with a 48 V electric golf cart motor. The 12 kg electric motor increases powertrain torque up to 25% during acceleration and recharges the snowmobile's battery pack during steady-state operation. Air pollution from the gasoline engine is reduced to levels far below current best available technology in the snowmobile industry. The four-stroke engine's closed-loop EFI system maintains stoichiometric combustion while dual three-way catalysts reduce NOx, HC and CO emissions by up to 94% from stock. In addition to the use of three way catalysts, the fuel injection strategy has been modified to further reduce engine emissions from the levels measured in the CSC 2004 competition.

Engine-out CO emission and fuel conversion efficiency were measured in a highly-dilute, low-temperature diesel combustion regime over a swirl ratio range of 1.44-7.12 and a wide range of injection timing. At fixed injection timing, an optimal swirl ratio for minimum CO emission and fuel consumption was found. At fixed swirl ratio, CO emission and fuel consumption generally decreased as injection timing was advanced. Moreover, a sudden decrease in CO emission was observed at early injection timings. Multi-dimensional numerical simulations, pressure-based measurements of ignition delay and apparent heat release, estimates of peak flame temperature, imaging of natural combustion luminosity and spray/wall interactions, and Laser Doppler Velocimeter (LDV) measurements of in-cylinder turbulence levels are employed to clarify the sources of the observed behavior.

Fuel film temperature and thickness were measured on the piston crown of a DISI engine under both motored and fired conditions using the fiber-based laser-induced fluorescence method wherein a single fiber delivers the excitation light and collects the fluorescence. The fibers were installed in the piston crown of a Bowditch-type optical engine and exited via the mirror passage. The fuel used for the fuel film temperature measurement was a 2×10-6 M solution of BTBP in isooctane. The ratio of the fluorescence intensity at 515 to that at 532 nm was found to be directly, but not linearly, related to temperature when excited at 488 nm. Effects related to the solvent, solution aging and bleaching were investigated. The measured fuel film temperature was found to closely follow the piston crown metal temperature, which was measured with a thermocouple.

A procedure has been developed to build system level predictive models that incorporate physical laws as well as information derived from experimental data. In particular a soot model was developed, trained and tested using experimental data. It was seen that the model could fit available experimental data given sufficient training time. Future accuracy on data points not encountered during training was estimated and seen to be good. The approach relies on the physical phenomena predicted by an existing system level phenomenological soot model coupled with ‘weights’ which use experimental data to adjust the predicted physical sub-model parameters to fit the data. This approach has developed from attempts at incorporating physical phenomena into neural networks for predicting emissions. Model training uses neural network training concepts.

An investigation of the effect of microflow machining on the spray characteristics of diesel injectors was undertaken. A collection of four VCO injector tips were tested prior to and after an abrasive flow process using a high viscosity media. The injector nozzles were tested on a spray fixture. Rate of injection measurements and high-speed digital images were used for the quantification of the air entrainment rate. Comparisons of the spray characteristics and A/F ratios were made for conditions of before and after the abrasive flow process. Results showed a significant decrease in the injection-to-injection variability and improvement of the spray symmetry. A link between the quantity of air entrained and potential differences in spray plume internal chemical composition and temperature is proposed via equilibrium calculations.

Neural networks are useful tools for optimization studies since they are very fast, so that while capturing the accuracy of multi-dimensional CFD calculations or experimental data, they can be run numerous times as required by many optimization techniques. This paper describes how a set of neural networks trained on a multi-dimensional CFD code to predict pressure, temperature, heat flux, torque and emissions, have been used by a genetic algorithm in combination with a hill-climbing type algorithm to optimize operating parameters of a diesel engine over the entire speed-torque map of the engine. The optimized parameters are mass of fuel injected per cycle, shape of the injection profile for dual split injection, start of injection, EGR level and boost pressure. These have been optimized for minimum emissions. Another set of neural networks have been trained to predict the optimized parameters, based on the speed-torque point of the engine.