The memristor has quite a reputation as a missing circuit element. It is a powerful candidate for next-generation applications after being first implemented in HP's laboratories. At this point, mathematical models were needed for the analysis of the memristor, and a lot of studies were done on this subject. In this chapter, mathematical modeling and simulations of the memristor device have been emphasized. Firstly, linear drift and nonlinear drift models have been described on the basic HP model. The window functions used in the nonlinear drift model have been widely examined. Different from HP model, the Simmons tunnel barrier and the threshold adaptive memristor model (TEAM) have been also mentioned. As a result, the most widely used modeling techniques have been described in detail.
Electric vehicles, which are an important part of sustainable energy technologies, occupy an important place in our daily life. More efficient use of electric vehicles will ensure more efficient use of sustainable energy sources. It is not possible for the human brain to determine the most efficient driving characteristics. In this study, energy efficient driving optimization of electric vehicles was realized. Along the route, optimum speeds were determined in terms of energy, by using the road and engine characteristics. Geographical information systems and genetic algorithm have been used effectively in the solution of the problem. The effectiveness of the proposed algorithm was revealed with many test studies. With this study, an algorithm that provides an energy-efficient driving for electrical vehicles was developed. The results will contribute to the development of electric vehicle technologies.
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