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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.

Cold start vehicle driveability performance depends on many parameters, one of which is the distillation character of the fuel. In the late 90's, a gasoline driveability index (DI) was developed for spark ignited combustion vehicles by a consortium of automotive and petroleum industry scientists based on correlation studies between controlled fuel quality matrices and vehicle performance under specific ambient conditions. The DI equation uses a weighted sum of gasoline distillation temperatures at the 10, 50 and 90 percent evaporation volumes, commonly called T10, T50 and T90. These three distillation volatility points are specified by the ASTM International D 4814 fuel specification and are seasonally adjusted. This paper studies the cold start driveability performance of Federal EPA Bin 5 and Bin 8 vehicles with respect to fuel distillation characteristics at temperatures other than T10, T50 and T90.

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.

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 objective of this paper is to propose that the automotive engineering “legacy” approach of assuming that vehicle electrical return paths using conductive structural components have zero (0) ohms of impedance, needs to be reviewed. The process in this review consists of the development of an equivalent circuit composed of both real (“resistive”) and reactive elements (due to inductance and capacitance). This issue needs to be addressed due to the increased complexity of electrical and electronics systems in vehicles and the use of the vehicle structural (such as chassis, frame, or bracket) components to provide a path for the return of electrical power and/or signals as a way to eliminate additional wiring manufacturing issues, cost, and weight contribution to a vehicle.

Various single and split injection schemes are studied to provide a better understanding of fuel distribution during cold starting in DI diesel engines. Improved spray-wall interaction, fuel film and multicomponent vaporization models are used to analyze the combustion processes. Better combustion characteristics are obtained for the split injection schemes than with a single injection. An analysis of the fuel impingement processes identifies the mechanisms involved in producing the differences in vaporization and combustion of the fuel. A greater amount of splashing occurred for the split injections compared to a single injection. This behavior is attributed to the decreased film thickness (less dissipation of impingement energy), the decreased impingement area (obtained by increasing the impingement Weber number), and most importantly, the reduced frequency of drop impingement.

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.

Aromatics, cetane number, density, and lubricity of a large number of retail diesel fuel samples were compared before, during, and after the switch from low sulfur to ultra low sulfur diesel fuel. Also, sulfur and nitrogen speciation was done on a small set of samples to determine the changes in heterocyclic compounds as refiners made the switch. For ultra low sulfur fuel, total aromatics decreased substantially, density decreased slightly, cetane number increased by about 1 - 2 numbers, and lubricity (wear scar) improved slightly relative to low sulfur diesel fuel. Speciation results showed that hydrotreating eliminated thiophenes and benzothiophenes from the fuel. The only sulfur species remaining were dibenzothiophenes, and these were present at very low part per million levels. Nitrogen compounds in the form of carbazoles were either eliminated or reduced to low part per million levels.

Implementation of engine turnoff at idle is desirable to gain improvements in vehicle fuel economy. There are a number of alternatives for implementation of the restarting function, including the existing cranking motor, a 12V or 36V belt-starter, a crankshaft integrated-starter-generator (ISG), and other, more complex hybrid powertrain architectures. Of these options, the 12V belt-alternator-starter (BAS) offers strong potential for fast, quiet starting at a lower system cost and complexity than higher-power 36V alternatives. Two challenges are 1) the need to accelerate a large engine to idle speed quickly, and 2) dynamic torque control during the start for smoothness. In the absence of a higher power electrical machine to accomplish these tasks, combustion-assisted starting has been studied as a potential method of aiding a 12V accessory drive belt-alternator-starter in the starting process on larger engines.

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.

Neural networks have been used for engine computations in the recent past. One reason for using neural networks is to capture the accuracy of multi-dimensional CFD calculations or experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame. This paper describes three methods to improve upon neural network predictions. Improvement is demonstrated for in-cylinder pressure predictions in particular. The first method incorporates a physical combustion model within the transfer function of the neural network, so that the network predictions incorporate physical relationships as well as mathematical models to fit the data. The second method shows how partitioning the data into different regimes based on different physical processes, and training different networks for different regimes, improves the accuracy of predictions.

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

This paper describes the transient spray characteristics of a high pressure, single fluid injector, intended for use in a direct-injection spark-ignited (DISI) engine. The injector was a single hole, pintle type injector and was electronically controlled. A variety of measurement diagnostics, including full-field imaging and line-of-sight diffraction based particle sizing were employed for spray characterization. Transient patternator measurements were also performed to obtain temporally resolved average mass flux distributions. Particle size and obscuration measurements were performed at three locations in the spray and at three injection pressures: 3.45 MPa (500 psi), 4.83 Mpa (700 psi), and 6.21 MPa (900 psi). Results of the spray imaging experiments indicated that the spray shapes varied with time after the start of injection and contained a leading mass, or slug along the center line of the spray.

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.

Recent advances in ANN (Artificial Neural Network) technology enable new methods to be developed in sensor technology. There are a large number of cases where there exists a causal relationship between one or more inputs and a physical quantity, but where an easily implemented analytical relationship between the inputs and the output can not easily be found. In such cases, machine learning techniques, such as artificial neural networks, are able to model that functional relationship. However, using conventional computing hardware, these methods, while theoretically attractive, are too computationally intensive for field deployment in real-time systems. Using a hardware implementation of an artificial neural network architecture, these computational restrictions can be eliminated.

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.

Airflow characteristics surrounding non-evaporating transient diesel sprays were investigated using a 6-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 hydraulically actuated, electronically controlled unit injector (HEUI) system was used to supply the fuel into a pressurized constant volume chamber at room temperature. The chamber gas densities in this study were 10 kg/m3, 20 kg/m3 and 30 kg/m3. The injection pressure was 96.5 MPa. Two frequency doubled (532 nm) Nd:YAG lasers were used to create coincident laser sheets to illuminate the test section at two instances after start of injection (ASI). The double exposed images of sprays and Al2O3 seed particles were developed and velocity vectors of the gas surrounding the transient diesel sprays were obtained using a numerical autocorrelation PIV method.

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.

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.