Paper Info
Title
Adaptive Energy Control Strategy for a Hybrid Energy Storage System in a DC Micro-Grid of an Unmanned Surface Vehicle.
Abstract
At present, the DC micro-grid power supply system based on new energy generation has become the primary developmental direction for improving the endurance of an unmanned surface vehicle (USV). In this study, an adaptive energy control strategy based on the moving average filtering algorithm is proposed to solve the severe impact of the pulsing load mutation on the hybrid energy storage system (HESS) in the DC micro-grid. The moving average filtering algorithm is used to filter the pulsating load power, and a battery slows the power change. Meanwhile, the super capacitor compensates for the instantaneous power mutation, optimizing the charge and discharge process of the battery. In addition, gain-varying adaptive control for the terminal voltage of the supercapacitor is adopted to stabilize it near the reference value, which solves the problem of voltage off-limit caused by the unequal output and absorption energy of the supercapacitor. The simulation results show that the proposed control strategy can effectively and quickly suppress the power fluctuation caused by the load mutation of the photovoltaic DC micro-grid system, improve the quality of the system output power, and enhance the reliability and stability of the system.
Year
DOI
Venue
2019
10.20965/jaciii.2019.p0287
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
Keywords
Field
DocType
DC micro-grid,hybrid energy storage,USV,new energy
Energy storage,Automotive engineering,Unmanned surface vehicle,Computer science,Micro grid,Artificial intelligence,Energy control,Machine learning
Journal
Volume
Issue
ISSN
23
2
1343-0130
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Zhongjiu Zheng1513.47
Yujia Xu200.34
Ning Wang320218.93
Hong Zhao410516.53