Author:
Chen Ao,Chen Tianding,He Hong,Wang Hui,Fan Yimei,Yu Xin
Abstract
Abstract
In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV’s equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations.
Reference27 articles.
1. Analysis of the application and development of integrated electric power technology for unmanned ships;Wang;Chinese Journal of Ship Research,2022
2. Key technologies and future development trends of unmanned surface vessels (in Chinese);Nie;Marine Equipment/Materials and Marketing,2022
3. Development and missions of unmanned surface vehicle;Yan;Journal of Marine Science and Application,2010
4. Energy management strategy of hybrid ship based on deep reinforcement learning (in Chinese);Chen;China Measurement and Test,2020