International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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AI-Based Optimization of Battery Management Systems for Enhanced Electric Aircraft Efficiency

Author(s) Yashi Garg
Country India
Abstract The aviation industry's shift towards sustainability has brought electric aircraft to the forefront as an eco-friendly alternative to traditional fossil-fuel-powered planes. Central to the efficiency and safety of these aircraft are Battery Management Systems (BMS), which ensure optimal performance through real-time monitoring and advanced energy management. This paper explores the role of AI-based optimization techniques in enhancing BMS functionality, focusing on predictive maintenance, dynamic energy distribution, and fault detection. By leveraging machine, deep, and reinforcement learning, these systems address key challenges such as energy inefficiency, battery degradation, and operational unpredictability. Case studies of electric aviation projects, including Eviation Alice and Rolls-Royce ACCEL, underscore the transformative potential of AI-driven BMS. Additionally, lessons from the electric vehicle industry highlight opportunities for cross-sector innovation. The research concludes that advanced BMS optimization is pivotal for the widespread adoption of electric aircraft, offering significant benefits in energy savings, extended battery life, improved safety, and reduced environmental impact.
Keywords Electric Aircraft, Battery Management Systems (BMS), AI Optimization, Sustainability, Predictive Maintenance, Energy Efficiency, Aviation Technology, Renewable Energy Integration
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-29
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31365
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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