Real-time peak power prediction for zinc nickel single flow batteries

The Zinc Nickel single flow batteries (ZNBs) have gained increasing attention recently. Due to the high variability of the intermittent renewable energy sources, load demands, and the operating conditions, the state of charge (SoC) is not an ideal indicator to gauge the potential cycling abilities. Alternatively, the peak power is more closely related to the instantaneous power acceptance and deliverance, and its real-time estimation plays a key role in grid-based energy storage systems. However, little has been done to comprehensively examine the peak power delivery capability of Zinc Nickel single flow batteries (ZNBs). To fill this gap, the recursive least square (RLS) method is first employed to achieve online battery model identification and represent the impact of varying working conditions. The state of charge (SoC) is then estimated by the extended Kalman filter (EKF). With these preliminaries, a novel peak power prediction method is developed based on the rolling prediction horizon. Four indices are proposed to capture the characteristics of the peak power capability over length-varying prediction windows. Finally, the consequent impacts of the electrode material and applied flow rate on peak power deliverability are analysed qualitatively

A Novel RBF Neural Model for Single Flow Zinc Nickel Batteries

As a popular type of Redox Flow Batteries (RFBs), single flow Zinc Nickel Battery (ZNB) was proposed in the last decade without requiring an expensive and complex ionic membrane in the battery. In this paper, a Radial Basis Function (RBF) neural model is proposed for modelling the behaviours of ZNBs. Both the linear and non-linear parameters in the model are tuned through a new feedback-learning phase assisted Teaching-Learning-Based Optimization (TLBO) method. Besides, the fast recursive algorithm (FRA) is applied to select the proper inputs and network structure to reduce the modelling error and computational efforts. The experimental results confirm that the proposed methods are capable of producing ZNB models with desirable performance over both training and test data