Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications
نویسندگان
چکیده
The continuous deployment of distributed energy sources and the increase in adoption electric vehicles (EVs) require smart charging algorithms. existing EV chargers offer limited flexibility controllability do not fully consider factors (such as user waiting time length next trip) well potential opportunities financial benefits from using EVs to support grid, charge renewable energy, deal with negative impacts intermittent generation. lack adequate may result high battery degradation, violation grid control statutory limits, greenhouse emissions, cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel advanced controller is proposed, which considers requirements, tariff, condition (e.g., voltage or frequency), (photovoltaic) output, state health. A rule-based fuzzy becomes complex number inputs increases. addition, it difficult achieve an optimum operation due conflicting nature requirements. To optimize response, PSO technique proposed provide global solution based on predefined cost function, address implementation complexity, combined neural network. algorithm meets works within technical constraints, simple implement real (and requires less processing time). Simulation MATLAB experimental results dSPACE digital real-time emulator are presented demonstrate effectiveness controller.
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ژورنال
عنوان ژورنال: IEEE Transactions on Industry Applications
سال: 2022
ISSN: ['1939-9367', '0093-9994']
DOI: https://doi.org/10.1109/tia.2022.3164999