Fazal U. Syed

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—Hybrid electric vehicles (HEVs) have attracted a lot of attention due to environmental and efficiency reasons. Typically, an HEV combines two power trains, a conventional power source such as a gasoline engine, a diesel engine, or a fuel cell stack, and an electric drive system (involving a motor and a generator) to produce driving power with a potential(More)
—With the increased emphasis on improving fuel economy and reducing emissions, hybrid electric vehicles (HEVs) have emerged as very strong candidates to achieve these goals. The power-split hybrid system, which is a complex hybrid powertrain, exhibits great potential to improve fuel economy by determining the most efficient regions for engine operation and(More)
Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system may assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g, lane departure) and alert the driver to take corrective action. In this paper, we show(More)
In this paper, we present a fuzzy logic based adaptive algorithm with a learning mechanism that estimates driver's long term and short term preferences. The algorithm represents a significant advancement to the capability of our previous non-adaptive real-time fuel economy advisory system that was implemented in a Ford Escape Hybrid [8][9]. This real-time(More)
—Power-split hybrid electric vehicles (HEVs) provide a great opportunity to improve fuel economy and emissions. This power-split hybrid system has inherent low damping in driveline since it uses planetary gear sets to directly connect the engine, the generator, and the motor to the driveline for improved vehicle efficiency, thus lacking a clutch or a torque(More)
Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative as the SVM methodology has not previously been attempted for(More)
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