The thesis is focused on remitting lead-acid segments of partial charge mode which simulates the conditions in HEV. The experimental cells were submitted to different pressures on the electrode system. The main aim of the thesis was to minimize the irreversible sulphating of the negative electrodes, which are in the PSoC regime limiting in the overall life of lead-acid accumulators. All cells were submitted to measurement of the negative electrode potentials, resistance of active materials, contact resistance of the grid – the active material and measurements of pressure changes during three PSoC cycles.

The first part of this work describes the design and implementation of switch-mode power supply for control circuits for 12/2x24V 4.5A fuel cells. The introduction picks the issue of power sources in electromobiles and final design and implementation of switching power suply. The next part deals with the issues of fast-charger and generators for power transistors. It describes the structure of DC converter and control and protectin circuits of quick-charger. In the end this work describes selected driver circuits and charge tests on fast-charger as evidenced by measurements.

This Master’s thesis deals with the issue of lead-acid batteries, which are used in hybrid electric vehicles. The lead-acid batteries works in mode PSoC. In this mode occurs to degradation mechanisms at negative electrodes. These degradation mechanisms reduce the battery life. The practical part of Master’s thesis describes the production and a compilation of experimental cells and experimental part examines the characteristics of lead-acid batteries with the pressure to the electrode system.

In this diploma project I´m designing golf cart for transportation two persons . At the side of design I wanted to achieve an innovate feature and diferentiate from today´s production. Through my conception I want to offer a wider possibility of usage and increase use value of golf cart. Herewith I mean for example an integration to the traffic after urgent modification. I dont want to design the golf cart like a vision to the future, but I endeavour for complying with current trends at the producing.

The main contribution of this work is a new perspective on design of a golf cart for 2 players. The aim is to create a cart which will be innovative in the design but retaining necessary technical, ergonomic, aesthetic and ecological requirements. The final draft should be able to compete to existing products in this market.

The U.S. transportation sector represented about 28% of all energy consumption in 2018. Petroleum products accounted for 92% of this total energy. Light-duty vehicles are the largest energy consumers in the transportation sector. The high amount of petroleum used by light-duty vehicles creates significant economic and environmental challenges. Electric Vehicles (EVs) have a higher fuel economy and can be emission-free; they are therefore an alternative solution for minimizing the negative environmental impact of internal combustion engine vehicles. However, the adoption of EVs has been limited by their limited driving range, long recharging time, and comparatively higher price. Dynamic wireless charging technology allows for charging the EV battery in motion. Charging pads are embedded in the road and the EV battery is charged while the vehicle is passing over them. This technology not only extends the EV range but also results in a considerable reduction in battery size and capacity. Therefore, dynamic wireless charging solves one of the major issues of EVs, leading to their large-scale adoption. In the first part of this dissertation, a pad optimization methodology is presented to minimize system cost and losses. Using this method, two pads are optimized, built and tested for charging the EV. In the next section, two methods are presented to estimate how much the EV is laterally misaligned with respect to the center of the charging pads. This helps to increase system efficiency and power transfer capability. Finally, new concrete-based material is presented and studied to reduce the charging pad cost and increase their durability.

<div>The accelerated urbanization of countries has led the adoption of the smart power grid with an explosion in high power usage. The emergence of Non-intrusive load monitoring (NILM), also referred to as Energy Disaggregation has followed the recent worldwide adoption of smart meters in smart grids. NILM is a convenient process to analyze composite electrical energy load and determine electrical energy consumption.</div><div><br></div><div>A number of state-of-the-art NILM (energy disaggregation) algorithms have been proposed recently to detect various individual appliances from one aggregated signal observation. Different kinds of classification methods such as Hidden Markov Model (HMM), Support Vector Method (SVM), neural networks, fuzzy logic, Naive Bayes, k-Nearest Neighbors (kNN), and many other hybrid approaches have been used to classify the estimated power consumption of electrical appliances from extracted appliances signatures. This study proposes an end-to-end edge computing system with an NILM algorithm, which especially focuses on recognizing Electric Vehicle (EV) charging. This system consists of three main components: (1) Data acquisition and Preprocessing, (2) Extraction of EV charging load via an NILM algorithm (Load identification) on the NILMTK Framework, (3) and Result report to the cloud server platform.</div><div><br></div><div>The monitoring of energy consumption through the proposed system is remarkably beneficial for demand response and energy efficiency. It helps to improve the understanding and prediction of power grid stress as well as enhance grid system reliability and resilience of the power grid. Furthermore, it is highly advantageous for the integration of more renewable energies that are under rapid development. As a result, countless potential NILM use-cases are expected from monitoring and identifying energy consumption in a power grid. It would enable smarter power consumption plans for residents as well as more flexible power grid management for electric utility companies, such as Duke Energy and ComEd.</div>

This compressed file consists of h5 and python files created to conduct the thesis study

Background:    Climate change has been becoming a major topic of interest, for research as well as for society. Transport caused emissions are constantly growing which forced the European Union to set the goal to decrease transport related emissions by 60% until 2050. A heavily discussed and promising tool seems to be being found in battery electric (BEV) vehicle adoption. However, BEV adoption seems to be underachieved which raises questions about potential influencing factors on BEV adoption. Additionally, latest research elaborated perception to be one of the key topics of interest for consumers adopting fully electric vehicles. Purpose:           The purpose of this thesis was to examine influencing factors affecting BEV adoption and the perception of those factors among generation Y consumers.  Method:            To attain the purpose, a qualitative research was conducted. After collecting secondary data to evaluate existing factors influencing consumer’s willingness to adopt BEVs, 16 participants accountable to generation Y have been interviewed using semi-structured interviews. Using a qualitative research approach valuable data and in-depth insights which are essential for markets such as the automotive industry. Conclusion:      The results show that there is a generally positive attitude towards BEV adoption among generation Y. However, there have been five influencing factors affecting consumer’s willingness to adopt BEVs. Analysis of the perception of each factor allowed the research team to get in-depth insights and to elaborate the importance of each factor and how the factors interrelate. Based on the gathered data relationships between influencing factors have been highlighted and based on TAM and TRI models a new model for further research has been developed.

Plug-in electric vehicles (PEV) can reduce greenhouse gas emissions. However, the utility of PEVs, as well as reduction of emissions is highly dependent on daily vehicle kilometres travelled (VKT). Further, the daily VKT by individual passenger cars vary strongly between days. A common method to analyse individual daily VKT is to fit distribution functions and to further analyse these fits. However, several distributions for individual daily VKT have been discussed in the literature without conclusive decision on the best distribution. Here we analyse three two-parameter distribution functions for the variation in daily VKT with four sets of travel data covering a total of 190,000 driving days and 9.5 million VKT. Specifically, we look at overall performance of the distributions on the data using four goodness of fit measures, as well as the consequence of choosing one distribution over the others for two common PEV applications: the days requiring adaptation for battery electric vehicles and the utility factor for plug-in hybrid electric vehicles. We find the Weibull distribution to fit most vehicles well but not all and at the same time yielding good predictions for PEV related attributes. Furthermore, the choice of distribution impacts PEV usage factors. Here, the Weibull distribution yields reliable estimates for electric vehicle applications whereas the log-normal distribution yields more conservative estimates for PEV usage factors. Our results help to guide the choice of distribution for a specific research question utilising driving data and provide a methodological advancement in the application of distribution functions to longitudinal driving data.