Browsing by Subject "Charging"

Now showing 1 - 2 of 2
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    Advanced Modeling and Control Strategies for Charging Electric Vehicle Batteries
    (2019-10) Pasha Khan, Murtaza Kamal
    The research in this master's thesis presents an advanced modeling and control strategy for charging electric vehicle (EV) batteries. The purpose of modeling the battery incorporating the optimal control mechanism is developing a fast-charging system for EVs. The thesis starts with a literature survey to find out the latest EV battery model within an appropriate format of interest. Then, on the selected battery model, it applies the state-dependent Riccati equation (SDRE) technique to develop a closed-loop optimal control strategy. For the purpose of optimization, the battery model aims to track a reference trajectory with a performance index which is minimizing the quadratic error between a reference and an actual trajectory. To harness the unified benefits of optimal and intelligent control systems, the thesis also sheds light upon fuzzy logic by generating a reference trajectory with it. Finally, to determine the correctness of the modeling, MATLAB simulations for a lithium-ion (li-ion) battery have been carried out and they display a satisfactory tracking performance.
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    Optimized Scheduling Of Electric Vehicle Charging And Discharging In A Vehicle-To-Grid System
    (2015-06) Hosseinpour, Shima
    The increase in electric vehicle (EV) demand and the associated electricity load on the power network have made researchers to start working on managing and controlling EVs' connection time to the electricity grid. Vehicle to grid concept was introduced to enable EVs to connect to the grid and discharge their extra electricity to the network so that the utility company could use it for regulation purposes. In this thesis, offline and online scheduling optimization models are developed for EV charging and discharging. The objective of the optimization models is to maximize the satisfaction of EV customers. Customer satisfaction is incorporated using different factors through multiple scenarios. In the offline model, all EVs and grid information are known for the V2G management to decide the scheduling for EVs. Mixed integer linear programming is used to solve the offline model. The result of the offline model is the optimum solution the scheduling problem could get. On the other hand in the online model, which is a more realistic case, EVs arrival and departure times and their parameters are not identified in advance. For this model, rolling horizon optimization is used in the online scheduling algorithm. Applying rolling horizon enables the author to get the optimal solution for the online model. Mixed integer linear programing is linked with a MATLAB algorithm to solve the online scheduling model. A numerical example, including a large number of EVs in a parking lot is generated to test the efficacy of both proposed models.