An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter

Abstract

It is crucial to conduct highly accurate estimation of the state of charge (SOC) of lithium-ion batteries during the real-time monitoring and safety control. Based on residual constraint fading factor unscented Kalman filter, the paper proposes an SOC estimation method to improve the accuracy of online estimating SOC. A priori values of terminal voltage were fitted using cubic Hermite interpolation. In combination with the Thevenin equivalent circuit model, the method of adaptive forgetting factor recursive least squares is used to identify the model parameters. To address the problem that the UKF method is strongly influenced by system noise and observation noise, the paper proposes an improved method of residual constrained fading factor. Finally, the effectiveness of this method was verified by the test of Hybrid Pulse Power Characteristic and Beijing Bus Dynamic Stress Test. Results show that under HPPC conditions, compared with other methods, the algorithm in the paper estimates that the SOC error of the battery remains between -0.38% and 0.948%, reducing the absolute maximum error by 51.5% at least and the average error by 62.7% at least. Moreover, under the condition of Beijing Bus Dynamic Stress Test the algorithm estimates the SOC error of the battery stays between -0.811% and 0.526%, and the SOC estimation errors are all within 0.2% after ten seconds of operation. Compared with other methods, the absolute maximum error can be reduced by 42.7% at least and the average error is reduced by 95% at least. And the test proves that the method is of higher accuracy, better convergence and stronger robustness.INDEX TERMS lithium-ion battery, state of charge estimation, Residual constraint fading factorunscented Kalman filter, adaptive forgetting factor recursive least square, cubic Hermite interpolation.

Parameter Estimation for Hot-spot Thermal Model of Power Transformers Using Unscented Kalman Filters

“…By using the a-priori estimations at instant k from ( 7) and (8) and the Kalman gain in (15), the respective a-posteriori estimations can be obtained from ( 16) and (17), both of which are necessary for the next step.…”

An error covariance correction-adaptive extended Kalman filter based on piecewise forgetting factor recursive least squares method for the state-of-charge estimation of lithium-ion batteries

“…It characterizes the ability of the adaptive filter to quickly reflect changes in the input characteristics. According to Equation (20), when the forgetting factor is large, the historical data account for a large, but it cannot track the changes of system parameters in real-time. When the forgetting factor is small, the historical data are easily forgotten to a large extent, and the tracking output changes are better, but the stability is poor.…”

“…In the second category, the best-known methods involve Kalman Filter (KF) [14], Extended Kalman Filter (EKF) [15][16][17], Unscented Kalman Filter (UKF) [18,19], Fading Kalman Filter (FKF) [20], Cubature Kalman Filter (CKF) [21,22], Unscented Particle Filter (PF) [23], H-infinity Observer [24], Sliding Mode Observer (SMO) [25] and so on. They utilize the battery equivalent model to describe the state-space equations, which has the advantages of adaptively reducing the influence of noise characteristics, simple calculations, and high accuracy [26].…”

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

“…Commonly used equivalent circuit models are classified as the RC model, the Thevenin model, the Rint model, the PNGV model and the GNL model. 19,20 Based on the ECM, the Kalman filter (KF) family method has become a main research technique for battery SOC estimation, 21,22 including: the extended KF algorithm, 23,24 the multiinnovation extended KF algorithm, 25,26 the unscented KF algorithm, 27,28 the dual KF algorithm, 29,30 and the cubature KF algorithm. 31,32 However, the KF family methods require high precision for the battery model, which is directly related to the model complexity.…”