Designing and Implementation of an Intelligent Energy Management System for Electric Ship power system based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract

Artificial Intelligence (AI) is a promising trend in ship energy management systems (EMS). The motivations of this work are designing and implementation of an intelligent energy management system for ship's electric power system based on an adaptive Neuro-Fuzzy Inference System (ANFIS) and the ship power source is an environmental friend system consists of proton exchange membrane fuel-cell (FCPM) considered as the main power source and battery bank as an electric storage system using (ANFIS) to manage the fuel cell generation by solving the optimization problem to reduce the Hydrogen fuel consumption and ensure the system balance. The benefit of using this technique is to penetrate a new field of using renewable and sustainable energy sources in marine to reduce greenhouse emission and increase the sailing period, system reliability by interfacing with the ship's integrated power system. The simulation of this system is carried out by MATLAB® software and (EMS) is implemented to test rig hardware with computer and interface card to emulate the ship's electric power system. The results obtained from the simulation are compared with the experimental results for the evaluation of the EMS performance.

Smart dual battery management system for expanding lifespan of wireless sensor node

“…ANFIS is also specifically used to calculate, analyze, and predict power control and energy management in critical applications such as aircraft, battery management systems, smart grids, and power system fault diagnostic. 7,20 ANFIS combines the capabilities of computational intelligence of both ANN and FIS, especially for nonlinear problems. ANN consists of interconnected nodes called artificial neurons that act like a human brain to process and communicate information.…”

“…The main objective of the ANFIS model is to charge the battery up to an upper voltage level and for a limited and predefined time to avoid overcharging and increase the lifespan of the battery. 20 Figure 8 shows the ANFIS model structure designed using MATLAB software. The ANFIS model is a system with a closed-loop control that depends upon the previous expert data.…”

“…The (3 Â 3474) expert data are entered into the system during the input and output training to develop the controller with the minimum error value. 20,21 In the proposed ANFIS model, epochs are executed to reduce the training error up to 0.165. It means the proposed system produces the desired result regarding the training data.…”

“…The CC-CV circuit with the linear regulator is designed using the components of an LM317 voltage regulator and three resistors (220, 515, and 12 Ohm) as shown in Figure 13. For designing the CC circuit, the value of resistance (R s ) is connected in series with LM317 IC and calculated by Equation (20). For designing the constant voltage source, a voltage divider circuit is connected with the LM317 IC.…”

A Review of Battery Charging - Discharging Management Controller: A Proposed Conceptual Battery Storage Charging – Discharging Centralized Controller

“…Furthermore, the modes also need to ensure the battery storages can perform better and improve the utilization of renewable energy systems [9,[13][14][15]. Looking at the importance of battery storage charging -discharging, methods or mechanisms such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) [6,16,17], backtracking search algorithm [10,18,19], non-simultaneous charging and discharging [20], genetic algorithm [4,21], particle swarm optimized fuzzy controller [22][23][24][25][26][27], model predictive control [28][29][30][31][32], dynamic optimal power flow [33][34][35][36], and grey model and genetic algorithms [37] are commonly used to perform the battery storage charging and discharging. The used methods or mechanisms also conduct system optimization to reduce the stress on the battery storages as well as protect the batteries from being damaged.…”