Predictive UAV Battery Maintenance Planning with Artificial Intelligence

Year 2025, Volume: 9 Issue: 2, 260 - 269, 28.06.2025

Hüseyin Şahin

https://doi.org/10.30518/jav.1546277

Abstract

This research paper explores the use of artificial intelligence (AI) in the maintenance planning of electric batteries for unmanned aerial vehicles (UAVs). Traditional maintenance strategies are challenged by the impact on battery performance and the complexity of battery degradation, highlighting the importance of an AI-assisted predictive maintenance approach. The research predicts battery degradation using machine learning techniques, specifically Artificial Neural Networks (ANN) model, in combination with MATLAB's Remaining Useful Life (RUL) Prediction Toolbox. The AI model is designed to accurately predict remaining flight time and perform maintenance only when needed. This prevents premature battery replacement, reduces environmental pollution, and contributes to sustainable aviation. The AI-powered maintenance model helps transform maintenance strategy, optimize operational costs, and increase the safety of UAV systems while reducing unexpected battery failures. Refined predictive methodologies for UAV battery diagnostics and maintenance demonstrate the importance of UAV battery health on operational efficiency. Statistical analysis of the AI model demonstrates robust predictive capability, achieving a mean absolute percentage error (MAPE) of 3.2% for battery capacity degradation and 2.9% for flight time prediction, supporting high prediction accuracy. The study’s originality lies in its use of ANN within the MATLAB RUL Prediction Toolbox to provide a data-driven predictive maintenance framework for UAV batteries, addressing a gap in the literature by offering a scalable solution that enhances prediction accuracy over traditional methods. The study proposes the integration of real-time operational data and advanced AI algorithms and demonstrates a significant advance in predictive maintenance to improve UAV reliability and sustainability.

Keywords

Predictive Maintenance, Unmanned Aerial Vehicle, Battery, Artificial Intelligent, Remaining Useful Life Estimation

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