Joint SOC/SOH estimation of all-vanadium flow batteries based on DAUKF algorithm

Enhancing the estimation accuracy of the internal state of Vanadium Redox Flow Batteries (VRBs) significantly impacts the management and control of energy storage systems. Accurately estimating the State of Charge (SOC) and State of Health (SOH) of VRBs is crucial. Focusing on the equivalent loss model of VRBs and considering the mutual influence between two state parameters, an enhancement to the conventional Adaptive Unscented Kalman Filter (UKF) is proposed. This enhancement introduces a Double Adaptive Unscented Kalman Filter (DAUKF) algorithm. The DAUKF algorithm adaptively estimates VRB’s SOC using the UKF and enables real-time calculation and model parameter updating for internal resistance via an adaptive UKF. This achieves joint estimation of VRB’s SOC and SOH, which is then compared and analyzed against the UKF algorithm. Experimental verification results demonstrate that this algorithm provides a more accurate estimation of the battery’s Ohmic internal resistance, consequently elevating the estimation precision of VRB batteries.