Endurance driven energy management system for all-electric marine autonomous surface vehicle

Zaman, Taimur and Syed, Mazheruddin Hussain and Burt, Graeme and Wahoud, Ali and Gobbo, Gianfranco and Millard, Garry and Malagodi, Stefano; (2022) Endurance driven energy management system for all-electric marine autonomous surface vehicle. In: IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society . IEEE, BEL. ISBN 978-1-6654-8025-3 (https://doi.org/10.1109/IECON49645.2022.9968947)

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Abstract

An autonomous eco-robotic Surface Vessel (ASV) is designed to operate in extreme weather conditions with an autonomy of several days or months. This research work aims to present a process for on-board power management between the vessel’s power sources, while maximizing the use of Renewable Energy Sources (RES) and taking into consideration onboard sensor, navigation and control and data transfer power requirements. A detailed architecture for a DC network integrating Photovoltaic (PV) panels, Fuel Cells (FCs), hydro generator and energy storage systems is developed. An efficient and flexible Energy Management System (EMS) is developed for managing power sources and maximising endurance using only clean energy. To assess the performance of EMS in meeting the energy demands of the Ocean drone’s equipment and propulsion systems, a simulation-based analysis is carried out for realistic missions and scenarios. The developed EMS strategy intends to harvest the energy from PV and hydrogenerator while maintaining the Battery Energy Storage Systems (BESS) as charged as possible. The developed power supply system architecture and EMS can jointly accommodate the need for efficient and long-lasting operation of the vessel with CO2-emission free energy sources.

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