SOC Prediction Research of VRB Based on Elman Neural Network
As an important part of the micro-grid, the energy storage devices are playing an important role to stabilize power and energy fluctuations of micro-grid system, improve the stability and schedulability of the system and the quality of electricity. All-Vanadium Redox Flow Battery (VRB) has been more and more used because of its advantages on environmentally friendly, long cycle life, safety and reliability, achieving economies of scale energy storage and so on. State of Charge (SOC) prediction is an important part to the Power Control System (PCS) and Energy Management System (EMS) of the energy storage devices. This paper established a SOC prediction model of VRB based on Elman Neural Network because Neural Network has the characteristics of non-linear and self-learning to simulate external characteristic of VRB which is a highly non-linear system. Then we trained the Elman Neural Network with the experimental data. The result shows that this method has a high accuracy. By contrast with the BP neural network, the prediction is better than Back Propagation (BP)Neural Network.
Prof. Kyle Jiang, Prof. Shinn-Liang Chang and Prof. Ruxu Du
H. X. Han et al., "SOC Prediction Research of VRB Based on Elman Neural Network", Applied Mechanics and Materials, Vol. 826, pp. 118-122, 2016