SOC Estimation Of Energy Storage Power Station Based On SSA-BP Neural Network

Abstract

Lithium battery State of Charge (SOC) estimation technology is the core technology to ensure the rational application of power energy storage, and plays an important role in supporting the maintenance and other operating functions of energy storage power stations. At present, the dynamic prediction of SOC is still It is a worldwide problem. This paper uses the BP neural network model as the basis and the sparrow search optimization algorithm to explore the prediction of the SOC of the energy storage lithium battery. The model uses NASA’s charge and discharge data of lithium batteries to train and predict the model to determine the feasibility of the BP network algorithm optimized by sparrow search.