Estimation of Power Battery State of Charge under Current Signal Sampling Deviation

doi:10.3901/JME.2023.16.288

Abstract

Abstract: In the actual operation of the electric vehicle battery management system, the current signal is susceptible to the interference of colored noise and the sampling signal deviation occurs, which causes the problem of a sharp drop in the accuracy of the SOC estimation. The SOC estimation problem of the current signal under the interference of two different types of colored noise is analyzed, and a three-layer combined estimation structure is proposed to realize the correction of the current sampling signal, the online update of the battery model parameters and the high-precision estimation of the SOC at the same time. Based on the second-order RC equivalent circuit model after state expansion, the model parameters are identified through adaptive recombination genetic algorithm, and the deviated current signal is corrected online by the adaptive square root cubature Kalman filter algorithm（ASRCKF）. Based on this, bias compensation forgetting factor recursive least squares algorithm（BCFRLS） and ASRCKF algorithm for collaborative estimation, to achieve online update of model parameters and SOC values. It is verified under DST conditions. The experimental and simulation results show that the proposed combined structure can still guarantee the high-precision estimation of the SOC value under the current sampling deviation, and its average relative error can be maintained below 1%.

Key words: lithium-ion battery, estimation of state of charge, colored noise, Kalman filter

CLC Number:

TG156

LIU Yiqun, LI Mengmeng, LIU Tao, YANG Na, LI Weihua, WANG Jianfeng. Estimation of Power Battery State of Charge under Current Signal Sampling Deviation[J]. Journal of Mechanical Engineering, 2023, 59(16): 288-299.

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