Intelligent Energy Management System for an all-electric ship based on adaptive neuro-fuzzy inference system

Abstract

The International Marine Organization (IMO) regulations forcing shipbuilders to use electric ships to reducing pollution emitted from ship engines. Renewable energy resources are the perfect solution to solve this issue. The ship’s hybrid energy power system consists of several non-homogeneous energy resources diesel generator, renewable energy source or more, energy-storing system, and may hydrogen source as fuel cells. The EMS manages and controls the balance between the different types of sources and loads demand to ensure system stability and dependability. In this paper, energy management strategy (EMS) for fuel cell and battery hybrid systems is an essential property of controlling power flow between sources. A crucial ingredient of an intelligent control strategy is to manage the flow of a hybrid system corresponding to the changing of the load demand and battery state of charge (SoC) using the ANFIS/Simulink toolbox implements a case study architecture. This paper proposes a hybrid energy source for use in a naval ship’s silent mode of operation while looking for submarines with a low acoustic signature based on adaptive neuro-fuzzy procedures. The main objective of this analysis is to investigate the efficiency of the proposed system preserving compared with practical results obtained from previous work. These studies provide valuable understandings into hybrid power systems’ flow of EMS on the marine field.

Keywords

• Ship electric power system
• All-electric ship
• Energy management system
• Integrated power system
• Micro-grid
• Artificial intelligence