Battery management in electric aviation: A bibliometric review of technologies, regulations, and international collaborations

Abstract

This paper presents a bibliometric analysis of research trends and gaps in battery management systems (BMS) for electric aviation, spanning 1997 to December 2024. Based on 500 publications, the study identifies academic collaborations, regulatory frameworks, thematic evolutions, and research hotspots. It also highlights the field's most critical documents, countries, authors, and affiliations. The results reveal three major research phases: foundational development (1997–2016), rapid growth (2017–2022), and advanced multidisciplinary exploration (2023–December 2024). UAVs emerge as a central focus throughout, with increasing emphasis on electrical take-off and landing (eVTOL) aircraft in the later stages. Machine learning (ML), fuzzy logic approaches for power and thermal control, and predictive models for state estimation and problem diagnostics are among the most significant breakthroughs in the domain. However, significant challenges persist in this research domain, including the absence of clear regulatory frameworks for ML integration, the scarcity of onboard BMS models for large-scale aircraft, and insufficient predictive models for thermal management in eVTOL aircraft. Additionally, limited international collaboration, particularly from the United States, restricts the potential for data diversity and global innovation. This review provides a roadmap for future research, emphasizing the need for adaptive, real-time BMS models, stronger international partnerships, and policy standardization to accelerate the field of electric aviation.

Keywords: Electric aircraft, Lithium-ion battery, Neural networks, State-of-charge, Temperature prediction, Urban air mobility, Electric aircraft, Electric vehicles, Fighter aircraft, Fuzzy logic, International cooperation.