Huang, Denggao (Univeristy of Portsmouth), Becerra, Victor M. (University of Portsmouth), Simandjuntak, Sarinova (University of Portsmouth), Ma, Hongjie (University of Portsmouth), Fraess-Ehrfeld, Alex (AIRBORNE ROBOTICS Limited)

An Intelligent BMS for Drone-Based Inspection of Offshore Wind Turbines

Scheduled for presentation during the Regular Session "Control Architectures II" (FrB1), Friday, June 24, 2022, 12:50−13:10, Asimon

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on April 20, 2024

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.