An innovative Dandelion Optimized Network Control (DONC) based effective energy management system for electric ships
Abstract
Energy Management System (EMS) plays a vital role in an international marine shipboard, due to their increased energy demand. The main purpose of this research work is to develop a new energy management system for satisfying the load demand of ship board applications. For accomplishing this objective, an advanced controlling mechanism, named as, Dandelion Optimized Network Control (DONC) is developed in this work. Also, the hybridized energy source including the fuel cell and battery storage are used in this design, where the fuel cell is main source of energy, and the battery storage is used as the supplementary storage device. Moreover, two different converter topologies such as interleaved zeta converter for fuel cell and bi-directional converter for battery storage are implemented in this study. The main purpose of using these converters are to effectively boost the output voltage of hybridized energy sources with reduced ripple current and distortions. The proposed DONC integrates the functions of DO technique as well as FNN for predicting the output power of fuel cell. During this process, the Fictitious Neural Network (FNN) technique obtains the input parameters of load demand power and battery SoC, and produces the predicted power of fuel cell for an effective energy management in electric ship board. In this mechanism, the weight value of FNN is optimally computed with the use of DO algorithm. The key benefits of the proposed DONC are increased efficiency, proper energy management according to the load demand, and reliable for ship applications. During simulation analysis, the load demand and fuel cell power are estimated with the normal, high, and low battery SoC states. The findings indicate that the proposed DONC can effectively manage and control the energy need of electric ship with the hybridized energy system.
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