A comparative study of the LiFePO4 battery voltage models under grid energy storage operation

Abstract

Lithium iron phosphate (LFP) batteries are widely used in energy storage systems (EESs). In energy storage scenarios, establishing an accurate voltage model for LFP batteries is crucial for the management of EESs. This study has established three energy storage working conditions, including power fluctuation smoothing, peak shaving, and frequency regulation. Four voltage models for commercial LFP batteries are developed, including the second-order resistor-capacitor equivalent circuit model, hysteresis voltage reconstruction model (HVRM), one-state hysteresis voltage model, and back-propagation neural network model. To evaluate model suitability in energy storage working conditions, we compare terminal voltage simulation accuracy, SOC estimation accuracy using the extended Kalman filter algorithm, and SOC calculation time. Overall, among the four models, the HVRM proves more suitable for energy storage scenarios, offering guidance for selecting an LFP voltage model in such conditions. Using the hysteresis model, we analyze the hysteresis open-circuit voltage (OCV) variations of LFP batteries in three energy storage scenarios. Research findings indicate that under frequency regulation, OCV exhibits high-frequency, small-amplitude variations, while under power fluctuation smoothing and peak shaving scenarios, OCV takes on a cyclic pattern. Considering the hysteresis OCV enhances Kalman gain matching, thereby improving SOC estimation accuracy.