Integration of a relaxation voltage prediction function into a PI-based observer to improve the SOC estimation of battery packs in renewable energy applications

Abstract

The integration of energy storage systems (ESSs) and specially of battery ESSs (BESSs) with renewable energy sources represents an efficient solution due to their ability to provide energy time shifting, peak shaving and so on. Considering the above, deepening all aspects related to the precise estimation of the state of charge (SOC) of a battery becomes extremely important. In this work, a real-time estimation algorithm including a PI-based observer and an equivalent circuit model (ECM), is improved exploiting the SOC dependence on the open circuit voltage (OCV). Particularly, it is described how the SOC can be reliably deduced from relaxation voltage in a short time thanks to a prediction function. The integration of such function into the PI-based observer allows to get a good trade-off between accuracy and complexity.