Dual-state estimation of electric vertical take-off and landing batteries: data-driven modeling and experimental verification

Purpose This study aims to develop and validate a robust data-driven framework for the simultaneous estimation of State of Health (SoH) and State of Charge (SoC) in lithium-ion batteries for electric vertical take-off and landing (eVTOL) aircraft. It specifically addresses the lack of comprehensive dual-state estimation and external experimental validation in existing aerial vehicle literature. Design/methodology/approach The study benchmarks ten regression algorithms using a high-fidelity open-access eVTOL mission data set. To ensure robustness and overcome overfitting risks, a novel “dual-level” validation strategy is used: predictions are cross-validated against a MATLAB/Simulink equivalent circuit model and further verified experimentally using data collected from six independently sourced Murata VTC-6 cells under varying aging cycles. Findings Extreme Gradient Boosting emerged as the superior model, offering the best trade-off between accuracy and computational efficiency. The model achieved a mean squared error of 0.2199 and root mean square error of 0.4688 for SoH, keeping capacity prediction errors below 2%. Furthermore, SoC estimation errors remained below approximately 1% across different aging cycles, demonstrating robust generalization across diverse operating conditions. Originality/value Unlike previous studies that rely solely on internal data splitting for validation, this research introduces an independent experimental verification layer using separate battery cells. This approach provides a transferable pathway for safe and reliable battery management system integration in electric aviation, bridging the gap between theoretical machine learning performance and real-world applicability.