Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries

Abstract

Batteries are key components in electric vehicles and energy storage systems. To estimate a battery’s state of charge, monitor its state of health, and formulate a balanced strategy, a battery model that requires a shorter and less costly study period than a real battery is established. This paper aims to describe a single-particle model of a lithium-ion battery that has a simple structure, can be embedded in simulation software for online applications, and provides a high-accuracy characterization of the dynamics. The single-particle model, which is described by a set of partial differential equations, is a simplified electrochemical model that characterizes the dynamic voltage response. A procedure for reducing the model based on the three-parameter polynomial approximation and the volume-average integration method is proposed to simplify the partial differential equations of the single-particle model. Identifying the parameters in the battery model is the key problem. The convergent bacterial foraging optimization algorithm with a short computation time is adopted for identifying the electrochemical parameters, including the active surface areas of the electrodes, the diffusion coefficients of the lithium ions in the solid phase, and the reaction rate constants. Then, the single-particle model of a lithium-ion battery is set up in MATLAB and Simulink. Finally, the precision of the single-particle model is verified by comparing the terminal voltages of the battery and the model. The results show that the single-particle model of a lithium-ion battery is very accurate and simple, thus verifying the reliability of the parameter identification process.