Online Model Identification Method of Vanadium Redox Flow Battery Based on Multiple Innovation Recursive Least Squares

Abstract

Vanadium redox flow battery (VRB) have a wide range of applications in renewable energy power generation, intelligent microgrids, and other fields due to their good safety and long cycle life. The internal parameters of vanadium redox flow battery will change in real time during operation. In order to ensure the reliable operation of the battery, an accurate electrical model needs to be established to monitor and predict the battery status in real time. In this paper, an RC equivalent model is established for vanadium redox flow battery, and the parameters are identified based on the model. In order to overcome the shortcomings of recursive least squares (RLS) algorithm, which is prone to data saturation and not tracking the time-varying parameters, this paper proposes a model parameter identification method based on the multiple innovation recursive least squares (MIRLS) algorithm. Based on the data collected in the pulse charging experiment, RLS and MIRLS algorithms were used to identify the model parameters. The experiments were performed on Wuhan University of Technology's 5kW / 3kWh VRB experimental platform, and the results show that the MIRLS algorithm has better convergence and accuracy in tracking time-varying parameters.