Battery storage is one of the key technologies in the transition toward net zero. Technology is quickly developing toward making energy grids digital. It is important to observe the condition of the battery in real time. A digital twin of battery storage is a virtual replica of a physical battery, which estimates and analyses battery operation and its state in real time. A battery digital twin consists of data collection, pre-processing, parameter estimation, modelling, and forecasting of the state of charge and state of health of the battery. This paper presents the concept and development of a digital twin for a lithium titanium oxide battery using a physics-based and data-driven model. A physics based Thevenin equivalent circuit model was developed. Experimental data from a lithium titanium oxide battery was collected from a robot application to analyze. Experimental data was used in a model-based state estimation approach of the Kalman filter for the state of charge estimation of battery storage. The state of health of the battery was estimated by the estimation of a decrease in total cell capacity and an increase in equivalent series resistance. The least square method was used to estimate total cell capacity. Equivalent series resistance was estimated using experimental voltage and current data. The output terminal voltage of the model is found to be well compared with experimental data.
Keywords Lithium titanium oxide battery, Kalman filter, cell capacity, terminal voltage, cell current