A review and bibliometric analysis of intelligent techniques for advanced battery state estimation in aviation propulsion systems
| dc.contributor.author | Osman, Abdeen Ahmed | |
| dc.contributor.author | Alkhedher, Mohammad | |
| dc.contributor.author | Ghazal, Mohammed | |
| dc.contributor.author | Ramadan, Mohamad | |
| dc.contributor.author | Mistarihi, Mahmoud | |
| dc.date.accessioned | 2026-01-22T07:46:44Z | |
| dc.date.available | 2026-01-22T07:46:44Z | |
| dc.date.issued | 2025 | |
| dc.description | The need for advanced batteries has grown exponentially in recent years, driven by the increasing integration of electric propulsion into emerging aviation technologies. These technologies include unmanned aerial vehicles (UAVs), electric vertical take-off and landing (eVTOL) aircraft, and hybrid-electric aircraft (HEA). UAVs have become essential across industries, from agricultural monitoring and package delivery to defense applications like surveillance and disaster response [1,2]. Their utility has driven rapid technological innovation, with electric propulsion at the forefront due to its low noise, operational flexibility, and reduced maintenance requirements [3]. Similarly, eVTOL aircraft are revolutionizing urban air mobility by enabling short-distance, on-demand transportation, aligning with global priorities for reducing urban congestion and emissions [4,5]. These aircraft promise quieter operations and greater efficiency than conventional transport methods, making them highly attractive for urban logistics and passenger mobility. HEA, designed primarily for regional and short-haul routes, aims to reduce emissions by blending traditional engines with electric propulsion. This combination improves fuel efficiency and reduces greenhouse gas emissions, making these aircraft pivotal in efforts to decarbonize aviation [[6], [7], [8]]. As the adoption of these aviation technologies grows, so does the demand for reliable, high-performing batteries that can meet operational challenges. This dependency underscores the critical role of batteries in enabling these transformative applications and their broader implications for the future of sustainable aviation [9,10]. | |
| dc.description.abstract | This review assesses advanced battery state estimation techniques for electric aviation, focusing on machine learning (ML), filtering methods, and fuzzy-based energy management strategies. Aviation batteries face unique challenges, including extreme fluctuations in power demand and significant variations in temperature and pressure across flight phases. These conditions disturb battery behavior and complicate state of charge (SOC), state of health (SOH), and remaining useful life (RUL) estimations. Filtering methods such as Kalman and particle filters demonstrate resistance to noise and dynamic loads. However, their application remains mostly limited to unmanned aerial vehicles (UAVs), with minimal studies addressing hybrid-electric aircraft (HEA) and none focused on electric vertical takeoff and landing (eVTOL) aircraft. ML techniques, including deep and hybrid models, offer adaptability under harsh conditions. Nevertheless, most studies rely on non-benchmarked or static-temperature datasets, limiting real-world relevance, especially in eVTOL aircraft applications. Transformer models outperform traditional deep learning approaches in low temperatures, showing promise for HEAs. Fuzzy-based techniques, while less suited for regression tasks, are widely adopted for energy management due to their ability to incorporate expert-defined logic via membership functions and rule-based control. However, most hybrid fuzzy systems lack interpretability evaluation, which poses a barrier to certification and deployment. This review highlights critical gaps, including the insufficient aviation-specific datasets, the underutilization of lithium polymer batteries in intelligent models, and the need for adaptive, context-aware estimation architectures tailored to dynamic aviation missions. Keywords: Long short-term memory, Neural networks, Physics-informed models, Random forest, Regression | |
| dc.identifier.citation | Osman, A. A., Mistarihi, M. Z., Ramadan, M., Ghazal, M., & Alkhedher, M. (2025). A review and bibliometric analysis of intelligent techniques for advanced battery state estimation in aviation propulsion systems. Results in Engineering, 106741. | |
| dc.identifier.doi | https://doi.org/10.1016/j.rineng.2025.106741 | |
| dc.identifier.uri | https://repository.adu.ac.ae/handle/1/8068 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier B.V. | |
| dc.title | A review and bibliometric analysis of intelligent techniques for advanced battery state estimation in aviation propulsion systems | |
| dc.type | Other |