Research For State Of Charge Estimation Algorithms Of Lithium-ion Battery Based On Equivalent Circuit Model

Because of the advantages of non-polluting and the best energy-to-weight ratios, Lithium-ion batteries are ubiquitous sources of energy for portable electronic devices. With the scarcity of traditional energy sources and the advocate of low-carbon economy, Large-capacity lithium-ion batteries have been used in the field of backup energy storage and electric vehicle power supply. To make an intensive study of Lithium-ion, A high-fidelity battery model capable of accurately predicting battery performance is significance to battery management and battery-powered system. The state of charge (SOC) of the battery is a very important state variable, and is closely related with the battery in life, safety and efficiency. Capturing a battery's SOC is a key technology of the BMS.Base on the study of battery model and parameter identification, we are committed to the SOC estimation of Lithium-ion battery. This paper introduces the working principle and characteristics of lithium-ion battery parameters, the charge and discharge, open circuit voltage, and the hybrid pulse power characteristic experiment have been done for the 18650 lithium-ion battery. Base on experimental analysis results and battery circuit model, a second-order RC equivalent circuit model considering temperature parameters is established. According to the results of the HPPC experiment, parameter identification is implemented using the nonlinear least squares under different condition of SOC. At the same time using the exponential fitting and polynomial fitting method to get the function relation between each parameter and SOC, and verify the battery the accuracy of the model parameters. Gaussian-Hermite quadrature filtering (GHQF) algorithm is projected to solve estimation of the state space equation in which SOC is one of the states. To validate the extracted model of the Lithium-ion battery, the simulation result and the experiment data are compared.The proposed electrical model can accurately predict runtime and voltage response of battery. The GHQF algorithm in the battery SOC estimation has high precision, robustness of the initial value and antijamming capability.