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

A lithium battery, battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of reduced filtering accuracy, filtering divergence, etc.

Inactive Publication Date: 2018-09-04
FUJIAN UNIV OF TECH
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Problems solved by technology

[0010] The present invention proposes a method for estimating the SOC of lithium batteries based on the improved EKF algorithm, which overcomes the problem of reduced filtering accuracy and even filter divergence in the EKF algorithm due to battery model errors and unknown statistical characteristics of noise, and simplifies the adaptive Kalman filter. Algorithm calculation

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

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Embodiment Construction

[0071] Implementation process of the present invention:

[0072] A method for estimating the lithium battery SOC of an improved EKF algorithm, comprising the steps of:

[0073] Step 1: Establish an equivalent model of a lithium battery:

[0074] Considering the dynamic characteristics and complexity of the battery model, this paper selects the second-order RC parallel Thevenin battery model, and its model structure is shown in figure 1 shown.

[0075] Where U is the battery terminal voltage, U ocv is the open circuit voltage, R 1 , C 1 are the resistance and capacitance of the activation polarization, respectively, R 2 , C 2 Respectively, the resistance and capacitance of the concentration polarization, the mathematical relationship of the model:

[0076] U=U ocv -R 0 I-U p1 -U p2 (1)

[0077]

[0078] Step 2: Model parameter identification method:

[0079] The model parameter identification method refers to the HPPC dynamic working condition experiment mentio...

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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...

Claims

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Application Information

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IPC IPC(8): G01R31/36
Inventor 刘成武邓青
Owner FUJIAN UNIV OF TECH
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