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Optimized Fuzzy Logic Controller for Responsive Charging of Electric Vehicles

机译:电动汽车响应充电的优化模糊逻辑控制器

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This paper contributes to the theme of intelligent integration of energy storage and the control of prosumer resources. The paper illustrates the use of a zero-order Sugeno fuzzy model to perform bidirectional real power control from an electric vehicle (EV), i.e., vehicle to grid (V2G) and grid to vehicle (G2V). The paper proposes an initial design of the fuzzy logic controller (FLC) following which the FLC is optimized using genetic algorithm (GA) for better performance under varying charging speed requirements of the user and better efficiency. A perturb and observe (P&O) algorithm based reactive power control is also proposed for voltage support through these EV chargers. The designed FLC can not only respond to grid conditions based on the time of use (TOU) but also to users charging speed requirements. Also, the initial design of an FLC may not suit a particular user’s requirement but can be optimized to meet the requirements.
机译:本文以储能智能集成和生产者资源控制为主题。本文说明了使用零阶Sugeno模糊模型来执行从电动汽车(EV),即车辆到电网(V2G)和电网到车辆(G2V)的双向有功功率控制。本文提出了模糊逻辑控制器(FLC)的初始设计,然后使用遗传算法(GA)对FLC进行了优化,以在用户变化的充电速度要求下具有更好的性能和更高的效率。还提出了一种基于扰动和观察(P&O)算法的无功功率控制,以通过这些EV充电器提供电压支持。设计的FLC不仅可以根据使用时间(TOU)响应电网状况,还可以响应用户的充电速度要求。另外,FLC的初始设计可能不适合特定用户的要求,但可以对其进行优化以满足要求。

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