An electrical model capable of estimating the state of energy for lithium-ion batteries used in energy storage systems

The state of energy (SOE) of Li-ion batteries is a key indicator for the energy management and optimization of energy storage systems (ESSs) in smart grids and electric vehicles (EVs). In order to improve the SOE estimation accuracy, an electrical Li-ion battery model is presented in this study against the dynamic loads and the rate energy effects of the battery. Firstly, in order to take into thorough consideration of the influencing factors in the SOE estimation process, a look-up table is merged with a 2
^nd
order RC battery model for accurately predicting the battery voltage characteristics and capturing the nonlinear rate energy effects to realize the high-fidelity battery SOE and runtime prediction; Secondly, a new method to separate the fast and slow dynamics of the 2
^nd
order RC battery model is developed and presented with a high-performance accuracy; Thirdly, commercial Li-ion batteries are tested at dynamic loads with various temperature to validate the effectiveness of the proposed model. The testing results show high accuracy and reliability of the proposed electrical battery model on the estimation of the battery SOE and the terminal voltage responses under dynamic loads.