A method for estimating SOC of lithium battery based on improved EKF algorithm

Abstract

The invention proposes a method for estimating SOC of a lithium battery based on the improved EKF algorithm. On the basis of the EKF algorithm, by means of the robust data correction idea, a residualek between the EKF observation variance mid-end voltage estimation value (shown in the description) and an actual measured value yk is tacked as the reference; an influence function is adopted, and athreshold Eta is set; the residual value between the end voltage estimation value and the actual measured value yk is compared with the threshold value Eta; a noise variance Qk of the filtering process is corrected in real time; the weight of the noise Qk estimation error is reduced, and the robust objective function reaches a minimum value; then the observed noise covariance matrix Rk is adjustedthrough dynamic intervals of different SOCs, and the influence of the model error on the SOC estimation accuracy can be reduced.

Description

technical field [0001] The invention belongs to the technical field of lithium batteries, and in particular relates to a method for estimating the SOC of a lithium battery with an improved EKF algorithm. Background technique [0002] Batteries are the main energy carrier and power source of electric vehicles, and they are also the main components of the electric vehicle body. Accurate estimation of battery SOC can not only improve the capacity utilization efficiency of batteries, but also prolong the service life of batteries. [0003] SOC is one of the most important parameters of the battery management system. Accurate estimation of SOC not only provides accurate remaining power for electric vehicle drivers, but also provides a basis for the management and control of the battery management system. At present, the most commonly used SOC estimation algorithm is usually the Kalman filter algorithm. The traditional Kalman filter is obtained under standard conditions and is an...

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