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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.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

The paper describes a methodology to co-simulate, with high fidelity, simultaneously and in one computational framework, all of the main vehicle subsystems for improved engineering design. The co-simulation based approach integrates in MATLAB/Simulink a physics-based tire model with high fidelity vehicle dynamics model and an accurate powertrain model allowing insights into 1) how the dynamics of a vehicle affect fuel consumption, quality of emission and vehicle control strategies and 2) how the choice of powertrain systems influence the dynamics of the vehicle; for instance how the variations in drive shaft torque affects vehicle handling, the maximum achievable acceleration of the vehicle, etc. The goal of developing this co-simulation framework is to capture the interaction between powertrain and rest of the vehicle in order to better predict, through simulation, the overall dynamics of the vehicle.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

This paper presents a fault-tolerant control (FTC) method for four-wheel independently driven and steered (4WIS/4WID) electric vehicles based on a reconfigurable control allocation to increase the flexibility for vehicle control and improve the safety of vehicle after the steering actuator fails. The proposed fault tolerant control method consists of the following three parts: 1) a fault detection and diagnosis (FDD) module that monitors vehicle steering condition, detects and diagnoses actuator failures; 2) an upper controller that computes the generalized forces/moments to track the desired vehicle motion and trajectory; 3) a reconfigurable control allocator that optimally distributes the generalized forces/moments to four wheels. The FTC approach based on the reconfigurable control allocation reallocates the generalized forces/moments among healthy steering actuators and driving motors once the actuator failures is detected.

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.

A fundamentally based quasi-dimensional NOx emission model for spark ignition direct injection (SIDI) gasoline engines was developed. The NOx model consists of a chemical mechanism and three sub-models. The classical extended Zeldovich mechanism and N₂O pathway for NOx formation mechanism were employed as the chemical mechanism in the model. A characteristic time model for the radical species H, O and OH was incorporated to account for non-equilibrium of radical species during combustion. A model of homogeneity which correlates fundamental dimensionless numbers and mixing time was developed to model the air-fuel mixing and inhomogeneity of the charge. Since temperature has a dominant effect on NOx emission, a flame temperature correlation was developed to model the flame temperature during the combustion for NOx calculation. Measured NOx emission data from a single-cylinder SIDI research engine at different operating conditions was used to validate the NOx model.

The paper focus on enhancing the braking safety and improving the braking performance of the tractor/trailer vehicle. A slip-rate-based braking force distribution algorithm is proposed for the electronic braking system of tractor/trailer combination vehicle. The algorithm controls the slip-rates of the tractor's rear wheels and the semi-trailer's wheels changing with the slip-rate of tractor's front wheels, making tractor's front wheels lock up ahead of the tractor's rear wheels and the semi-trailer's wheels. The algorithm protects the combination vehicle from jackknifing and swing, guaranteeing that the combination vehicle has better driving stability and steering capability. The algorithm can be tested by co-simulation with MATLAB/Simulink and TruckSim software both on high adhesion and low adhesion roads.

The objective of this study was to investigate the effects of fuel viscosity and the effects of nozzle inlet configuration on the characteristics of high injection pressure sprays. Three different viscosity fuels were used to reveal the effects of viscosity on the spray characteristics. The effects of nozzle inlet configuration on spray characteristics were studied using two mini-sac six-hole nozzles with different inlet configurations. A common rail injection system was used to introduce the spray at 90 MPa injection pressure into a constant volume chamber pressurized with argon gas. The information on high pressure transient sprays was captured by a high speed movie camera synchronized with a pulsed copper vapor laser. The images were analyzed to obtain the spray characteristics which include spray tip penetration, spray cone angle at two different regions, and overall spray Sauter Mean Diameter (SMD).

We have applied an imaging system to a spray in an engine-fed combustion bomb to investigate some of the features of diesel spray ignition. A high pressure electronic unit injector with main and pilot injection features was used. Our interest in this work was the local air/fuel ratio, particularly in the vicinity of the spray plumes. The measurement was made by seeding the air in the intake manifold with biacetyl. A tripled ND:YAG laser causes the biacetyl to fluoresce with a signal that is proportional to its local concentration. The biacetyl partial pressure was carefully controlled, enabling approximate estimates of the local stoichiometry in the fuel spray. Twenty-four different cases were sampled. Parameters varied include swirl ratio, fuel quantity, number of holes in the fuel nozzle and distribution of fuel quantities in the pilot and main injections. This paper presents the results of three of these cases.

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 presents some experimental results of air velocity measurements near high pressure diesel sprays. The measurements were made using a moderately high pressure (90 MPa) common rail injector in a pressurized spray chamber. The chamber was operated at ambient temperature (25°C) and was pressurized with Argon to produce a chamber gas density of about 27 kg/m3, similar to densities found in a large turbocharged diesel near TDC. The gas phase was tagged using water droplets doped with Stilbene 420, with an estimated droplet size of 18 μm. The atomized water-Stilbene droplets were illuminated with the third harmonic of a pair of Nd:YAG lasers which caused the Stilbene to fluoresce at about 420 nm. To reduce the competing fluorescence from the injected fuel, the injector was fueled with Jet-A fuel. Using the two lasers, double exposures of the small droplets were recorded on film. The laser pulse lengths were about 6 ns, and typical times between pulses were 100 μs.

Airflow characteristics surrounding evaporating transient diesel sprays inside a constant volume chamber under temperatures around 1100 K were investigated using a 6-hole injector and a single-hole injector. Particle Image Velocimetry (PIV) was used to measure the gas velocities surrounding a spray plume as a function of space and time. A conical control surface surrounding the spray plume was chosen as a representative side entrainment surface. The normal velocities crossing the control surface toward the spray plume for single-hole injection sprays were higher than those of 6-hole injection sprays. The velocities tangential to the control surface toward the injector tip for the single-hole injection sprays were lower than those of 6-hole injection sprays. An abrupt increase in tangential velocities near the chamber wall suggests that the recirculation of surrounding gas was accelerated by the spray wall impingement, both for non-evaporating and evaporating sprays.

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.

This paper proposed a novel fault-tolerant control method based on control allocation via dynamic constrained optimization for electric vehicles with XBW systems. The total vehicle control command is first derived based on interpretation on driver's intention as a set of desired vehicle body forces, which is further dynamically distributed to the control command of each actuator among vehicle four corners. A dynamic constrained optimization method is proposed with the cost function set to be a linear combination of multiple control objectives, such that the control allocation problem is transformed into a linear programming formulation. An analytical yet explicit solution is then derived, which not only provides a systematic approach in handling the actuation faults, but also is efficient and real-time feasible for in-vehicle implementation. The simulation results show that the proposed method is valid and effective in maintaining vehicle operation as expected even with faults.

Four-wheel independent drive EV is driven by four brushless DC motors which are embedded in the wheel hubs. It enables each wheel's driving torque to be controlled independently. Due to the motors' torque and rotational speed easily measured, as well as the features of fast response and precise control, the EV enjoys obvious advantages over traditional vehicles in acceleration slip regulation. In this paper a novel acceleration slip regulation strategy for four-wheel independent drive EV is studied. The strategy includes a road identification module for the peak value of road adhesion coefficient and a slip regulation logic based on PID algorithm. Through comparing the current wheel slip ratio and the utilized adhesion coefficient with the typical roads' value, the identification module adopts the fuzzy control algorithm to recognize the similarity between the current road and the typical roads. Utilizing the similarity we can calculate the optimal slip ratio of the current road.

Electric Power Steering System (EPS) can directly provide auxiliary steering torque via a motor. The motor and the reducer in mechanical system will make the friction torque in steering system larger, as a result, the ability of steering returning will be reduced. Therefore, during the design of EPS system control strategy, an extra active return-to-middle control strategy is needed. For the fact that most of the low-end vehicles equipped with EPS system do not have a steering wheel angle sensor, a control strategy has to work without the datum of steering wheel angle. This paper proposes an active return-to-middle control method without steering wheel angle sensor, based on the estimated aligning torque which is converted to the pinion, and expounds how to determine the steering system current motion state in detail. This control method will work just during the turning condition, so it has no effect on the EPS basic assist characteristics.

Steering movement is the most basic movement of the vehicle, in the car driving process, the driver through the steering wheel has always been to control the direction of the car, in order to achieve their own driving intention. Four Wheel Steering (4WS) is an advanced vehicle control technique which can markedly improve vehicle steering characteristics. Compared with traditional front wheel steering vehicles, 4WS vehicles can steer the front wheels and the rear wheels individually for cornering, according to the vehicle motion states such as the information of vehicle speed, yaw velocity and lateral acceleration. Therefore, 4WS can enhance the handling stability and improve the active safety for vehicles.