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Diagnosis of Supercapacitor State-of-Charge in Electric Vehicle Applications using Artificial Neural Networks | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 4، دوره 8، شماره 3، آذر 2025، صفحه 255-263 اصل مقاله (1.65 M) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2024.48015.1542 | ||
| نویسندگان | ||
| Seyed-Saeid Moosavi-Anchehpoli* 1؛ Mahmood Moghaddasian2؛ Maryam Golpour1 | ||
| 1Clean power generation and Electrochemical laboratory, Amol University of Special Modern Technologies, Iran | ||
| 2Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Iran | ||
| چکیده | ||
| In an electric vehicle, energy storage systems (ESSs) are critical for sinking and sourcing power as well as ensuring operational protection. Because of their high power density, quick charging or discharging, and low internal loss, supercapacitors (SCs) are a recent addition to the types of energy storage units that can be used in an electric vehicle as an energy storage systems. They can be used in conjunction with batteries or fuel cells to create a hybrid energy storage device that maximizes the benefits of each component while minimizing the disadvantages. This paper presents a multilayer perceptrons (MLP) feedforward artificial neural network for supercapacitor state-of-charge diagnosis in vehicular applications. The proposed approach is tested using a supercapacitor Maxwel model that is subjected to complex charge and discharge current profiles as well as temperature changes. The proposed wavelet neural network and the validation results significantly improves state-of-charge estimation accuracy in different current discharge profiles. | ||
| کلیدواژهها | ||
| State-of-charge (SOC)؛ wavelet transform (WT)؛ Artificial neural network (ANN)؛ Supercapacitor (SC) | ||
| مراجع | ||
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