The state of energy (SOE) of Li-ion batteries is a key indicator for the energy optimization and management of energy storage devices (ESDs) in electric vehicles (EVs) and smart grids. To improve the SOE estimation accuracy, a hybrid 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 the advantages of both models, a 2nd order RC battery model is merged with an analytical kinetic battery model for accurately predicting the battery voltage characteristics and capturing the nonlinear rate energy effects to realize the high-fidelity SOE and runtime prediction. Secondly, a new method to separate the fast and slow dynamics of the 2nd order RC model is developed and presented with high-performance accuracy. Thirdly, commercial Li-ion batteries are tested at dynamic loads under various temperature to validate the effectiveness of the proposed model. The experimental results show high accuracy and reliability of the proposed battery model on the estimation of the battery SOE and the battery terminal voltage responses under dynamic loads.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.