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In an attempt to reach a compromise between the views of environmentalists and snowmobile enthusiasts, the University of Wisconsin-Madison Clean Snowmobile Team set out to design a machine that maintains performance while decreasing air and noise pollution. After careful consideration of all possible design avenues, the decision was made to select a four-stroke power plant. In order to optimize the engine's efficiency, an engine control unit was chosen that was both capable and affordable. Engine modifications were made to allow the snowmobile's stock transmission to be used. Alterations were also made to intake, exhaust, and cooling systems to allow the engine to fit comfortably under the snowmobile's stock hood. Modifications were made to the snowmobile's chassis to accommodate the additional mass associated with the four-stroke engine. The final product is a snowmobile that minimizes environmental impact but still has the appearance and performance necessary to satisfy consumers.

A system was developed which provides nutrients and water to plants while maintaining good aeration at the roots and preventing water from escaping in reduced gravity. The nutrient solution is circulated through porous tubes under negative pressure and moves through the tube wall via capillary forces into the rooting matrix, establishing a non-saturated condition in the root zone. Tests using prototypes of the porous tube water and nutrient delivery system indicate that plant productivity in this system is equivalent to standard soil and solution culture growing procedures. The system has functioned successfully in short-term microgravity during parabolic flight tests and will be flown on the space shuttle. Plants are one of the components of a bioregenerative life support system required for long duration space missions.

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.

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.

This paper describes a computational and experimental effort to document the detailed flow field around a pickup truck. The major objective was to benchmark several different computational approaches through a series of validation simulations performed at Clemson University (CU) and overseen by those performing the experiments at the GM R&D Center. Consequently, no experimental results were shared until after the simulations were completed. This flow represented an excellent test case for turbulence modeling capabilities developed at CU. Computationally, three different turbulence models were employed. One steady simulation used the realizable k-ε model. The second approach was an unsteady RANS simulation, which included a turbulence closure model developed in-house. This simulation captured the unsteady shear layer rollup and breakdown over the front of the hood that was expected and seen in the experiments but unattainable with other off-the-shelf turbulence models.

This paper proposes a novel allocation-based control method for four-wheel independently actuated electric vehicles. In the proposed method, both actuator dynamics and input/output constraints are fully taken into consideration in the control design. First, the actuators are modeled as first-order dynamic systems with delay. Then, the control allocation is formulated as an optimization problem, with the primary objective of minimizing errors between the actual and desired control outputs. Other objectives include minimizing the power consumption and the slew rate of the actuator outputs. As a result, this leads to frequency-dependent allocation that reflects the bandwidth of each actuator. To solve the optimization problem, an efficient numerical algorithm is employed. Finally the proposed control allocation method is implemented to control a four-wheel independently actuated electric vehicle.

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.

Computational Fluid Dynamics (CFD) has gained popularity as a tool for many airflow situations including road vehicle aerodynamics. This trend, to bring CFD to bear on vehicle aerodynamic design issues, is appropriate and timely in view of the increasing competitive and regulative pressures being faced by the automotive industry. For a large portion of the engineering community, the primary source of CFD capabilities is through the purchase of commercial CFD codes. This paper summarizes the results of a series of benchmark external aerodynamic simulations that were carried out for a generic pickup truck model using two commercial CFD codes, namely Fluent and the PowerFLOW. For direct comparisons the computations and the experiments were performed for the same model (vehicle) geometry and under similar flow conditions.

The ASTROCULTURE™ (ASC) middeck flight experiment series was developed to test and integrate subsystems required to grow plants in reduced gravity, with the goal of developing a plant growth unit suitable for conducting quality biological research in microgravity. Flights on the Space Shuttle have demonstrated control of water movement through a particulate rooting material, growth chamber temperature and humidity control, LED lighting systems and control, recycling of recovered condensate, ethylene scrubbing, and carbon dioxide control. A complete plant growth unit was tested on STS-63 in February 1995, the first ASC flight in which plant biology experiments were conducted in microgravity. The methods and objectives used for control of environmental conditions in the ASC unit are described in this paper.

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 hybrid vehicle team has designed and constructed a four-wheel drive, charge sustaining, parallel hybrid-electric sport utility vehicle for entry into the FutureTruck 2003 competition. This is a multi-year project utilizing a 2002 4.0 liter Ford Explorer as the base vehicle. Wisconsin's FutureTruck, nicknamed the ‘Moolander’, weighs 2000 kg and includes a prototype aluminum frame. The Moolander uses a high efficiency, 1.8 liter, common rail, turbo-charged, compression ignition direct injection (CIDI) engine supplying 85 kW of peak power and an AC induction motor that provides an additional 60 kW of peak power. The 145 kW hybrid drivetrain will out-accelerate the stock V6 powertrain while producing similar emissions and drastically reducing fuel consumption. The PNGV Systems Analysis Toolkit (PSAT) model predicts a Federal Testing Procedure (FTP) combined driving cycle fuel economy of 16.05 km/L (37.8 mpg).

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.

Three distinct types of diesel particulate matter (PM) are generated in selected engine operating conditions of a single-cylinder heavy-duty diesel engine. The three types of PM are trapped using typical Cordierite diesel particulate filters (DPF) with different washcoat formulations and a commercial Silicon-Carbide DPF. Two systems, an external electric furnace and an in-situ burner, were used for regeneration. Furnace regeneration experiments allow the collected PM to be classified into two categories depending on oxidation mechanism: PM that is affected by the catalyst and PM that is oxidized by a purely thermal mechanism. The two PM categories prove to contribute differently to pressure drop and transient filtration efficiency during in-situ regeneration.

An experimental study was performed to investigate diesel particulate filter (DPF) performance during filtration with the use of real-time measurement equipment. Three operating conditions of a single-cylinder 2.3-liter D.I. heavy-duty diesel engine were selected to generate distinct types of diesel particulate matter (PM) in terms of chemical composition, concentration, and size distribution. Four substrates, with a range of geometric and physical parameters, were studied to observe the effect on filtration characteristics. Real-time filtration performance indicators such as pressure drop and filtration efficiency were investigated using real-time PM size distribution and a mass analyzer. Types of filtration efficiency included: mass-based, number-based, and fractional (based on particle diameter). In addition, time integrated measurements were taken with a Rupprecht & Patashnick Tapered Element Oscillating Microbalance (TEOM), Teflon and quartz filters.

Diesel particulate filters are designed to reduce the mass emissions of diesel particulate matter and have been proven to be effective in this respect. Not much is known, however, about their effects on other unregulated chemical species. This study utilized source dilution sampling techniques to evaluate the effects of a catalyzed diesel particulate filter on a wide spectrum of chemical emissions from a heavy-duty diesel engine. The species analyzed included both criteria and unregulated compounds such as particulate matter (PM), carbon monoxide (CO), hydrocarbons (HC), inorganic ions, trace metallic compounds, elemental and organic carbon (EC and OC), polycyclic aromatic hydrocarbons (PAHs), and other organic compounds. Results showed a significant reduction for the emissions of PM mass, CO, HC, metals, EC, OC, and PAHs.

Diesel exhaust particulate trap system is one of the most effective means to control diesel particulate emissions from diesel vehicles. In this paper, a recently developed diesel exhaust particulate trap system was described and experimentally studied. This system employed a wall-flow ceramic foam filter, which was made of silicon carbide or chromium oxide. And this system was equipped with a microwave heater for the purpose of filter regeneration. Engine dynamometer testing, vehicle bench testing and on-road evaluation of this system were conducted. The experiments studied on the filtration efficiency of this system, the effectiveness of filter regeneration, the power penalty of the vehicle, the ability of noise suppression of this system, and the durability of this particulate trap system. The experimental results showed that this diesel particulate trap system was effective, reliable, and durable.

This paper identifies important engine, alternator and battery characteristics needed for determining an appropriate engine control strategy for a series hybrid electric vehicle Examination of these characteristics indicates that a load-leveling strategy applied to the small engine will provide better fuel economy than a power-tracking scheme An automatic energy management strategy is devised whereby a computer controller determines the engine-alternator turn-on and turn-off conditions and controls the engine-alternator autonomously Battery state of charge is determined from battery voltage and current measurements Experimental results of the system's performance in a test vehicle during city driving are presented

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.

The ASTROCULTURE™ (ASC) middeck flight experiment series was developed to test subsystems required to grow plants in reduced gravity, with the goal of developing a plant growth unit suitable for conducting quality biological research in microgravity. Previous Space Shuttle flights (STS-50 and STS-57) have successfully demonstrated the ability to control water movement through a particulate rooting matrix in microgravity and the ability of LED lighting systems to provide high levels of irradiance without excessive heat build-up in microgravity. The humidity and temperature control system used in the middeck flight unit is described in this paper. The system controls air flow and provides dehumidification, humidification, and condensate recovery for a plant growth chamber volume of 1450 cm3.

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.