AI-based intelligent energy storage using Li-ion batteries

Suciu, George; Badicu, Andreea; Beceanu, Cristian; Yumlu, M.Serdar; Kaya, Yalçın; Kizak, Kadir Gurkan; Tahtasakal, Fatih

doi:10.1109/atee52255.2021.9425328

Cited by 5 publications

(1 citation statement)

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“…AI can improve overall efficiency by optimizing charging and discharging cycles, and minimizing energy losses. (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31) In this study, our focus is to develop a high-capacity 3.84 kW battery specialized cargo drone tailored for the transport of emergency goods specifically in challenging terrains such as mountainous areas and highways. The envisioned drone is designed with a targeted payload capacity of 50 kg to convey essential supplies swiftly and efficiently to locations that are typically hard to access via conventional transportation means.…”

Section: Introductionmentioning

confidence: 99%

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

Hartono,

Oh,

Lim

et al. 2024

Sensors and Materials

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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 realworld applications.

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