State of charge estimation for a UAV battery via Adaptive Extended Kalman Filter and Deep Reinforcement Learning

This study presents the development of an accurate State of Charge (SoC) estimation method for lithium-ion batteries used in mission- and safety-critical applications. The proposed approach integrates an Adaptive Extended Kalman Filter (AEKF) with Deep Reinforcement Learning (DRL) in order to enhanc...

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Egile Nagusiak:	Ahmet Can Erdem, Volkan Mert, Muharrem Aykan Külünk, Mehmet Sinan Oruç, Tuncay Altun, Derya Ahmet Kocabas
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Elsevier 2026-05-01
Saila:	Engineering Science and Technology, an International Journal
Gaiak:	Adaptive Extended Kalman Filter (AEKF) Battery management system (BMS) Deep Reinforcement Learning (DRL) Lithium-ion batteries (li-ion) Proximal policy optimization (PPO) State of charge (SoC)
Sarrera elektronikoa:	http://www.sciencedirect.com/science/article/pii/S2215098626000959
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