Real-time state of charge and capacity estimations of vanadium redox flow battery based on unscented Kalman filter with a forgetting factor

The monitoring of the state of charge (SOC) and capacity of the vanadium redox flow battery (VRFB) is challenging due to the complex electrochemical reactions. In addition, the apparent nonlinearity and time-varying nature of the battery increase the difficulty of monitoring. Herein, we propose an unscented Kalman filtering approach with a forgetting factor, which considers the impact of the battery's historical states on the current state without remarkably increasing computational cost, to observe the battery SOC precisely. By accurately estimating the SOC, the exact capacity is acquired with the ampere-hour method, which has extreme precision requirements for the SOC. To implement the proposed method, an equivalent circuit model with a first-order branch is constructed to catch the VRFB dynamics without considering its intricate chemical behaviors. Moreover, the recursive least-squares approach is used to extract the model parameters online to improve model accuracy. Experiments were carefully conducted on a self-assembled VRFB with the constant current charge profile and the defined hybrid pulse discharge profile to validate the novel method under five charge-discharge cycles. The results denote that the proposed method enables faster estimation convergence conditions as well as realizes accurate high-fidelity monitoring of the SOC and capacity with higher stability after convergence under the same conditions than other methods.