Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/5F7B4E00-DB56-4409-8AC8-6E3BC9C8CA99 | ||
| Year | 2022 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Meletios Gioumatzidis, "Optimal management of electric power flow charging – discharging and thermal management of batteries for electric vehicles and isolated grids applications ", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.92015 | ||
| Appears in Collections |
In recent years, electric vehicles (EVs) have attracted a great deal of worldwide attention as a viable solution for clean transport. Electric vehicle technology is not new since it was introduced in the mid-19th century. Low battery capacity, driving range, and superior gasoline-powered cars led to the collapse of electric cars in the 1930s. The efficiency and durability of battery technology is improving rapidly and, as battery costs are reduced, allow electric cars to compete in the market. There are many optimization techniques for optimally programming EV battery charging, which can be combined to form hybrid algorithms, for also optimal and perhaps even better results, using iterative algorithms, which are described in this review work. Optimization models are configured exclusively for the application of quadratic programming in order to reduce the cost of charging and were presented. Studies have been presented on the basis of fast charging infrastructure to avoid stress on the EV owner, as well as to create a smart charging program to reduce the negative effects of integrating a large EV fleet into the distribution network, in addition to alleviating environmental concerns. In the bibliographic study that served as the basis for the present bibliographic work, the studies presented are essentially done with the following simulation designs:• Four optimization models for charging and discharging EV, without a smart charging program, in two different scenarios. Peripheral load control and area load control, i.e. four case studies, to verify the accuracy of optimization models for maximum energy savings and load smoothing, using EV batteries to support utilities.• Case study that achieves peak load smoothing in the peripheral control load, as a scenario.• The remaining three studies have the desired results in the area load control strategy.
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