Advanced Real-Time Battery State Estimation for a Hybrid Aircraft

Abstract

Canada's aviation industry aims to reduce its carbon footprint while maintaining safety and operational standards. Hybrid and electric aircraft offer a promising solution to reducing the environmental impact of conventional aviation. However, current limitations in battery technology and the robustness of battery management systems in monitoring and estimating battery states cause significant challenges for their adoption in aviation applications. This research focuses on developing an advanced state of charge (SoC) estimation method for Li-ion batteries used in hybrid aircraft. A new approach is proposed that integrates a merged set of equivalent circuit models capable of simulating battery dynamics at both the cell and module levels. In addition, an innovative dual filter involved in interactive multiple model (IMM) frameworks is introduced, equipped with advanced filtering approaches such as smooth variable structure filter (SVSF). This framework ensures accurate SoC estimation even under varying and harsh operating conditions by testing all developed algorithms in real-time. The developed model is much more accurate than the existing SoC estimation algorithms in the aircraft battery management system. These findings increase the understanding of battery performance in specific aviation conditions and help develop safer and more advanced condition monitoring and estimation methods for hybrid and electric aircrafts.