An intelligent BMS for drone-based inspection of offshore wind turbines

Denggao Huang*, Victor Becerra, Hongjie Ma, Sarinova Simandjuntak, Alexander Fraess-Ehrfeld

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

As they are agile and versatile flying platforms, drones can be very useful for inspecting infrastructure and can significantly improve the safety and efficiency of these tasks. The lithium-ion battery is very often used as the power source of multi-rotor drones, a fundamental and core component that is determinant for the success of flying tasks. Because of the properties of the chemical reactions in lithium-ion batteries, proper battery management is often required in many applications to assure the system's safety. However, drone battery management systems have drawn little attention from researchers so far. Given the complexities associated with the ageing process of the lithium-ion battery, the incorrect capacity estimation is likely to lead to the unnecessarily early replacement of batteries. This study proposes a practical, intelligent battery management system (i-BMS) for drone batteries. The system requirements, architecture, hardware, software and algorithmic aspects of i-BMS are illustrated. Considering the communication requirements of the flight controller and automatic charging platform, an Internet of Things module is combined in the proposed i-BMS, which can provide multiple communication protocols. An unscented Kalman filter algorithm was applied to each cell in the drone battery to reinforce the confidence of the state of charge and state of health estimation. Experiments were conducted to validate the proposed i-BMS. The proposed i-BMS can precisely monitor every cell and successfully estimate the state of charge (SOC) and state of health (SOH) of the battery.
Original languageEnglish
Title of host publicationProceedings of the 2022 International Conference on Unmanned Aircraft Systems (ICUAS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1210-1218
ISBN (Electronic)9781665405935
ISBN (Print)9781665405942
DOIs
Publication statusPublished - 26 Jul 2022

Publication series

NameIEEE ICUAS Proceedings Series
PublisherIEEE
ISSN (Print)2373-6720
ISSN (Electronic)2575-7296

Keywords

  • Battery management system
  • drone
  • power management
  • SOC/SOH
  • unscented Kalman filter

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