Optimization
Seyed-Saeid Moosavi-Anchehpoli; Mahmood Moghaddasian; Maryam Golpour
Abstract
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 ...
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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.
