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The model, design, and management of renewable energy harvesting systems for maritime robots

The model, design, and management of renewable energy harvesting systems for maritime robots
The model, design, and management of renewable energy harvesting systems for maritime robots
Renewable energy harvesting systems could provide sustainable power to supply mobile robots for fully autonomous operation. However, the lack of model theory and design technique in current research limit the renewable energy system to the stationary application only. This thesis proposes a methodology to model, design and manage the power system for maritime robots. A novel non-stationary model including both global and local motion are developed for renewable energy harvesting systems. This offers a new methodology to simulate the power system with a spatial-temporal power generation and demand load model. The model is utilised by a data-driven approach to design a reliable and economic renewable energy system configuration under the size constraint. This design method is based on the optimisation approach to size the configuration of renewable energy devices. Furthermore, a learning-based power management strategy was proposed to improve the long term robustness of the power system. Variation and uncertainty in the renewable energy resource are mitigated by learning to plan the power usage from experience. The non-stationary renewable energy harvesting model shows both global and local motion of the robot influence the power generation and demand load. To match the power generation and demand load of the robot, the size of the renewable energy harvester and storage device has to be optimally designed under strict size constraints. Design optimisation result demonstrated the optimality and feasibility of the proposed renewable energy harvesting system configuration method. For the long-term performance, the learning-based power management strategy outperforms all benchmarking strategies in providing a guaranteed minimum level of power supply. Research results show the renewable energy could provide sustainable power supply to the robot. These methods and techniques provide a foundation to the model, design and management of renewable energy harvesting systems for mobile maritime robots.
University of Southampton
Cao, Yu
1a78aac1-22dc-4912-9147-4b58dd780ece
Cao, Yu
1a78aac1-22dc-4912-9147-4b58dd780ece
Townsend, Nicholas
3a4b47c5-0e76-4ae0-a086-cf841d610ef0

Cao, Yu (2020) The model, design, and management of renewable energy harvesting systems for maritime robots. Doctoral Thesis, 196pp.

Record type: Thesis (Doctoral)

Abstract

Renewable energy harvesting systems could provide sustainable power to supply mobile robots for fully autonomous operation. However, the lack of model theory and design technique in current research limit the renewable energy system to the stationary application only. This thesis proposes a methodology to model, design and manage the power system for maritime robots. A novel non-stationary model including both global and local motion are developed for renewable energy harvesting systems. This offers a new methodology to simulate the power system with a spatial-temporal power generation and demand load model. The model is utilised by a data-driven approach to design a reliable and economic renewable energy system configuration under the size constraint. This design method is based on the optimisation approach to size the configuration of renewable energy devices. Furthermore, a learning-based power management strategy was proposed to improve the long term robustness of the power system. Variation and uncertainty in the renewable energy resource are mitigated by learning to plan the power usage from experience. The non-stationary renewable energy harvesting model shows both global and local motion of the robot influence the power generation and demand load. To match the power generation and demand load of the robot, the size of the renewable energy harvester and storage device has to be optimally designed under strict size constraints. Design optimisation result demonstrated the optimality and feasibility of the proposed renewable energy harvesting system configuration method. For the long-term performance, the learning-based power management strategy outperforms all benchmarking strategies in providing a guaranteed minimum level of power supply. Research results show the renewable energy could provide sustainable power supply to the robot. These methods and techniques provide a foundation to the model, design and management of renewable energy harvesting systems for mobile maritime robots.

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Published date: September 2020

Identifiers

Local EPrints ID: 448874
URI: http://eprints.soton.ac.uk/id/eprint/448874
PURE UUID: da938def-bc6d-4c44-81d8-63c530d2f3dd
ORCID for Yu Cao: ORCID iD orcid.org/0000-0001-5767-7029

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Date deposited: 07 May 2021 16:33
Last modified: 12 Jul 2021 04:01

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Contributors

Author: Yu Cao ORCID iD
Thesis advisor: Nicholas Townsend

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