Quantifying the emissions due to the charging of the batteries of the plug-in hybrid electricity school bus operated by the Austin Independent School District (AISD) is the focus of this work. This plug-in hybrid school bus is one of only nineteen in the country, and was manufactured by IC Corporation. This hybrid school bus reduces fuel consumption and CO₂, NOx and PM emissions in comparison with conventional diesel buses. This reduction is good for the health of the children who take the school bus everyday as they are part of the population the most at risk because their lungs are still developing. In order to evaluate CO₂, NOx and PM emissions, measurements of the charging of the batteries versus time were taken for the two charging periods each day during the months of April and May 2009. These measurements were repeated in October 2009 when the route was changed for the new school year: 2009-2010. An analysis of the Austin electricity mix that provides electricity to the bus center was done hour by hour to evaluate the emissions, calculated on a g/mile basis. Measurements of the vehicle speed versus time and altitude were taken during February 2010 in order to explain some of the differences observed between the two routes. Different parameters were studied to analyze the results. The first parameter studied was the impact of the season on the emissions. The second parameter studied was the impact of the route and its characteristics (road length, traffic, grade, etc.). The last parameter studied was the difference between two methods used to evaluate the emissions using the electricity mix data. These two methods are different but each of them brings something to the analysis of the results. / text

While a lot of efforts have been on outdoor intelligent systems, internal living environment system that suits the occupancy’s behaviour has not received much attention. The intelligent living environment designed in this study has three components; the physical world (environment), the database and the decision maker. The study sought to design a model that senses ever changing home conditions such as lights, doors and windows. Other variables that were looked at include, but not limited to the number of people in the room and inside thermodynamics and human activity. Global information such as temperature, gas or electricity usage and time of the day will also be received by the system through various sensing facilities. The information will be sent to a rules engine for a decision on an appropriate action to be taken. The action may include just turning off the lights, in the case of a mild abnormality or a high alert to an emergency response unit in a most severe case. The study proposes a context aware and proactive neural networks control system to control a living environment with a main focus on the aged citizens living alone. The proposed living environment was not developed to an actual or “mock” building containing a representation of subset of sensors, actuators and controllers as used in the actual systems due to lack of funding. However, the study will report on the modelling and simulation of the home system variables based on the chosen Artificial Intelligent technique using MATLAB/SIMULINK. These results indicate a possibility of implementing the designed living environment to increase the resident’s security. / Electrical and Mining Engineering / M. Tech. (Engineering: Electrical)