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Notice of retraction
Vol. 34, No. 8(3), S&M3042

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Sensors and Materials, Volume 36, Number 4(2) (2024)
Copyright(C) MYU K.K.
pp. 1441-1459
S&M3610 Research Paper of Special Issue
https://doi.org/10.18494/SAM5011
Published: April 19 , 2024

Design of AI-based 3.84 kW Battery Package Using Backpropagation Artificial Neural Network Algorithm for Cargo Drones [PDF]

Rodi Hartono, Sang Min Oh, Sung Won Lim, Tshibang Patrick A. Kalend, Jasurbek Doliev, Jun Hyuk Lee, and Kyoo Jae Shin

(Received February 1, 2024; Accepted March 25, 2024)

Keywords: backpropagation artificial neural network, battery management system, battery cell balancing, charge/discharge state estimation, cargo drones, emergency transportation system

Despite limitations in payload and range, cargo drones have promising applications in emergency logistics and remote delivery. In this study, we tackle these challenges by developing a high-capacity 3.84 kW battery specifically designed for a 50-kg-payload cargo drone operating in demanding terrains. Focusing on the transport of emergency goods, we investigate key drone design aspects and details of the battery pack development, including cell selection, internal configuration, and critical circuits for cell balancing, charging/discharging, and advanced battery management. A key innovation is the integration of a backpropagation artificial neural network (BPANN) algorithm to predict the depth of discharge (DoD) and the state of charge (SoC). Research results show that BPANN offers highly accurate predictions, with error percentages as low as 0.12% for DoD and 0.02% for SoC, ensuring optimized and safe battery operation. Comprehensive field testing is carried out to evaluate the effectiveness of the proposed cell balancing strategy, robust battery management system (BMS), and BPANN implementation. We investigate the drone’s performance in terms of DoD, SoC, and overall field operation with the designed battery pack and demonstrate its feasibility and potential for real-world applications.

Corresponding author: Kyoo Jae Shin


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Cite this article
Rodi Hartono, Sang Min Oh, Sung Won Lim, Tshibang Patrick A. Kalend, Jasurbek Doliev, Jun Hyuk Lee, and Kyoo Jae Shin, Design of AI-based 3.84 kW Battery Package Using Backpropagation Artificial Neural Network Algorithm for Cargo Drones, Sens. Mater., Vol. 36, No. 4, 2024, p. 1441-1459.



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