Enhanced Electrothermal State Estimation and Experimental Validations for Electric Flying Car Batteries

Urban air mobility (UAM) has become an emerging urban travel paradigm due to its viability of alleviating traffic congestion and reducing commute time. Accurate and robust knowledge of key battery states, such as state of charge (SOC), state of temperature (SOT), and state of power (SOP), is a prerequisite for the safe, efficient, and reliable operation of electric flying cars. In this article, we propose an enhanced multistate joint estimation framework for collaboratively capturing the battery SOC, SOT, and SOP under typical UAM cycling profiles, where the underlying couplings among these states are investigated explicitly. The battery SOC is estimated by combining an adaptive extended Kalman filter with a simple electrical model calibrated in real-time. The SOT estimation is achieved through an adaptive Kalman filter and a control-oriented two-dimensional spatially resolved thermal model, and then the estimation accuracy is further enhanced by a gated recurrent unit network. Based on key electrothermal state estimations, the instantaneous and continuous dynamic power capability assessments are fulfilled considering multiple operational constraints. A temperature-constrained peak power assessment method, with the electrical parameters updated online, enables more accurate and reliable SOP estimation. The influence of battery SOT on peak power assessments is thoroughly investigated. The proposed estimation framework is validated against the experiments under various typical UAM profiles and ambient temperatures, demonstrating satisfactory accuracy and resilience.