2022
DOI: 10.1109/tia.2022.3164999
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Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

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Cited by 6 publications
(3 citation statements)
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“…Note that the analysis is based on the available hourly data. Once higher resolution data become available together with longer time series of measured data, more advanced algorithms for peak shaving and smart control, e.g., using neural network architectures [29] and load forecasting [30], will be tested and implemented.…”
Section: Limitations Of the Smart Charge Modelmentioning
confidence: 99%
“…Note that the analysis is based on the available hourly data. Once higher resolution data become available together with longer time series of measured data, more advanced algorithms for peak shaving and smart control, e.g., using neural network architectures [29] and load forecasting [30], will be tested and implemented.…”
Section: Limitations Of the Smart Charge Modelmentioning
confidence: 99%
“…In the proposed algorithm, EV dis/charging is influenced by PV uncertainty, dis/ charging priorities and electricity price, and it's able to reduce energy costs by 50%. A practical test of a smart charging controller based on PSO-ANN-Fuzzy is conducted by Ali et al [5], which considers user needs, energy tariff, grid conditions (for example, voltage or frequency), renewable (PV) output, and battery's state of health (SOH) status. The intended structure has RES and BES and is connected to the threephase power grid from one side.…”
Section: Introductionmentioning
confidence: 99%
“…To sum up, Table 1 compares the major features in existing researches with those of presented in this article where the relevant researches can be categorized into three general types: power electronics interface topology [3,6], control systems [9,12] and EMS strategies [2,4,5,7,8,10,11]. In general, it has been observed that in some of these papers, EMS strategies and control schematics have not been verified through experimental tests.…”
Section: Introductionmentioning
confidence: 99%