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@article{168922,
        author = {K.Suryanarayanamurthy},
        title = {Design Of PV Powered Electric Vehicle Charging Station  Using ANFIS Controller For Energy Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2160-2169},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168922},
        abstract = {The focus of this paper is on effectively managing power distribution among various sources. With the escalating concerns of global warming and climate change due to the surging demand for modern transportation systems, there is a burgeoning advocacy for Electric Vehicles (EVs) to address these issues. However, relying solely on fossil fuel-based infrastructure for charging EVs is neither economically viable nor efficient. This underscores the substantial potential of utilizing renewable energy sources for EV charging stations. To meet this requirement, the integration of a solar-powered EV charging station with a Battery Energy Storage System (BESS) is imperative. To ensure consistent power supply without straining the grid, it is advisable to incorporate additional grid support. Furthermore, an LLC was specifically designed at the grid to minimize the current distortion introduced into the utility grid. To achieve optimal power management across the solar panels, BESS, grid, and EVs, a well-structured charging station employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) with voltage-controlled Maximum Power Point Tracking (MPPT), PID control, and Neural Network techniques has been developed and assessed using MATLAB/Simulink.},
        keywords = {Electric Vehicles, Battery energy storage system, Adaptive Neuro-Fuzzy Inference System, MPPT, PID controller, Neural Network, PV.},
        month = {October},
        }