The SOC estimation of lithium-ion battery based on extended Kalman filtering algorithm

Abstract In the fields of new energy vehicles, energy storage systems, and electronic devices, the battery management system is one of the key technologies to ensure the performance and safety of the battery pack, and the estimation of the battery state of charge (SoC) is an important part of it. To solve the problem of the difficulty in accurately estimating the SoC of current lithium-ion batteries, this paper selects the dual-polarization circuit model to represent the external electrical characteristics of the battery, and identifies the model parameters. Then, under the New European Driving Cycle (NEDC) and Federal Urban Driving Schedule (FUDS) operating condition data, the Extended Kalman Filter (EKF) is used to estimate the SoC of the lithium-ion battery. The simulation results show that this SoC estimation method has the characteristics of high accuracy, fast convergence speed, and good robustness, which verifies the accuracy and effectiveness of the lithium-ion battery bipolar equivalent circuit model combined with the EKF algorithm to estimate the SoC of the lithium-ion battery.