The purpose of this work is to outline the first steps taken towards the building of an automatic interpretation and hypothesis generation machine. The contents of this thesis describe the framework built to parse and manipulate the knowl- edge assemblies encoded in BEL, which enables BEL to act as a semantic inte- gration layer for heterogeneous data and knowledge sources, the development of a framework for automatic integration of relevant knowledge from structured sources, and the development of schema-free analytical techniques to generate data-driven hypothesis.
With rapid electrification of transportation , it is becoming increasingly important to have a comprehensive understanding of criteria used in motor selection.For that design and comparative evaluation of interior permanent magnet synchronous motor ,induction motor and switched reluctance motor are needed.A fast finite element analysis (FEA) modeling approach is addressed for induction motor design.Optimal turn off and turn on angles with current chopping control and angular position control are found for Switched Reluctance Motors (SRM).Noise Vibration and Harshness (NVH) analysis are done using workbench ANSYS analysis.Simulation and analytical results show that each motor topology demonstrates its own unique characteristics for Electric Vehicle / Hybrid Electric Vehicle.Each motor's highest efficiency is located at different torque-speed regions for the criteria defined.Stator geometry ,pole/slot combination and control strategy differentiate Noise Vibration and Harshness performance.