EKF-Based SOH Estimation and Adaptive Output Control for Early Thermal Runaway Prediction in LiPo Batteries
Lee, Mu-Won
In high-maneuverability FPV drone operations, instantaneous discharge currents reaching tens to hundreds of amperes significantly elevate the risk of LiPo battery thermal runaway. This study proposes a software-based system that combines an Equivalent Circuit Model (ECM) with an Extended Kalman Filter (EKF) to estimate battery State of Charge (SOC) and State of Health (SOH) in real time, and applies Arrhenius reaction kinetics to predict time to thermal runaway. Using the NASA PCoE Li-ion battery dataset (B0005–B0018, 168 cycles), five comparative experiments were conducted: 1RC vs 2RC ECM, Coulomb counting vs EKF SOC estimation, R0 based vs capacity-based SOH definition, temperature threshold vs Arrhenius alert, and fixed vs adaptive output control. Results show that EKF-based SOC estimation achieved 22% lower RMSE than Coulomb counting, and Arrhenius-based alerts were triggered earlier and more stably within each cycle compared to temperature threshold methods. The adaptive output limiter reduced cumulative risk exposure time by 34%, quantitatively demonstrating thermal runaway prevention effectiveness. Furthermore, a physics-based endurance simulator executing a hardcoded FPV freestyle maneuver sequence was implemented, confirming that an SOH=1.00 battery reaches critical state in approximately 10.5 days and an SOH=0.70 battery in approximately 4.0 days.