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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 role of mixture stratification on combustion rate has been investigated in a constant volume combustion vessel in which mixtures of different equivalence ratios can be added in a spatially and temporally controlled fashion. The experiments were performed in a regime of low fluid motion to avoid the complicating effects of turbulence generated by the injection of different masses of fluid. Different mixture combinations were investigated while maintaining a constant overall equivalence ratio and initial pressure. The results indicate that the highest combustion rate for an overall lean mixture is obtained when all of the fuel is contained in a stoichiometric mixture in the vicinity of the ignition source. This is the result of the high burning velocity of these mixtures, and the complete oxidation which releases the full chemical energy.

Powertrain simulation is becoming an increasingly valuable tool to evaluate new technologies proposed for future military vehicles. The powertrain of the M1A1 Abrams tank is currently being modeled in the Powertrain Control Research Laboratory (PCRL) at the University of Wisconsin-Madison. This powertrain model is to be integrated with other component models in an effort to produce a high fidelity simulation of the entire vehicle.

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.

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 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.

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).

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.

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

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.

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.

For competition in the 1998 FutureCar Challenge (FCC98), the University of Wisconsin - Madison FutureCar Team has designed and built a lightweight, charge sustaining, parallel hybrid electric vehicle by modifying a 1994 Mercury Sable Aluminum Intensive Vehicle (AIV), nicknamed the Aluminum Cow. The Wisconsin team is striving for a combined, FTP cycle gasoline-equivalent fuel economy of 21.3 km/L (50 mpg) and Ultra Low Emissions Vehicle (ULEV) federal emissions levels while maintaining the full passenger/cargo room, appearance, and feel of a full-size car. To reach these goals, Wisconsin has concentrated on reducing the overall vehicle weight. In addition to customizing the drivetrain, the team has developed a vehicle control strategy that both aims to achieve these goals and also allows for the completion of a reliable hybrid in a short period of time.

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.

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.

A new diesel exhaust particulate trap system was developed to control diesel particulate emissions from buses in large cities in China. This system was equipped with a microwave heater for the purpose of filter regeneration. To achieve effective and efficient filter regeneration, a radio-frequency (RF) technology was employed. The RF technology measured the amount of particulate trapped in filter, and it controlled filter regeneration using microwave signal. In this paper, the on-line diesel particulate measurement system was described, and experimental study of this measurement system was reported. The experimental results proved the effectiveness of the RF technology in the application of this diesel particulate trap system.

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 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.