关键词: SOC, RLS算法, DLUKF, 参数辨识, 二阶RC电路

Abstract:

With the lithium cobalt oxide battery as the research object, to solve the problem of the ineffective prediction of the state of charge (SOC) due to drastic changes in the battery current under complex working conditions, we established a double-layer unscented Kalman filter (DLUKF) architecture based on accurate parameters to estimate the value of SOC accurately. First, the functional relationship and characteristic curve between the open circuit voltage and SOC of the battery in the second-order circuit model were obtained from the experimental data of hybrid pulse power characteristics. Second, the recursive least squares method was applied to accurately identify the unknown variables in the model online to enhance the adaptive learning capability in the process of model identification and solve the problem of inaccurate model parameter estimation. Finally, the unknown variables in the model were identified accurately based on the output value of the model. Then, the DLUKF algorithm was used to realize the prediction of SOC fast to solve the problem of inaccurate estimation and large error of a single UKF algorithm in a strong nonlinear system. This paper compared the DLUKF algorithm with the single UKF algorithm under UDDS and FUDS conditions. The SOC curve, SOC error curve, terminal voltage curve, and terminal voltage error curve estimated by the two algorithms were compared. The results showed that the average error of DLUKF algorithm was lower than that of UKF algorithm, and when comparing predictions, the result of DLUKF algorithm was more accurate.

Key words: SOC, RLS algorithm, DLUKF, parameter identification, second-order RC circuit