Lithium battery model parameter recognition method through least square method with forgetting factor

A least square method and forgetting factor technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problem that the recursive results cannot reflect new data well, and achieve the effect of overcoming data saturation and alleviating the superposition of old data

Inactive Publication Date: 2017-11-21
DONGGUAN DRN NEW ENERGY
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Problems solved by technology

However, in parameter identification, the recursive least squares method is an algorithm with infinite memory length. For the battery system, the least squares method has more and more old data during the recursive operation process, which will cause the recursive results to fail to reflect the new data well. characteristics

Method used

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  • Lithium battery model parameter recognition method through least square method with forgetting factor
  • Lithium battery model parameter recognition method through least square method with forgetting factor
  • Lithium battery model parameter recognition method through least square method with forgetting factor

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Embodiment Construction

[0027] Refer to attached figure 1 to attach figure 2 The specific embodiment of the present invention is introduced.

[0028] A method for identifying parameters of a lithium battery model by a least squares method with a forgetting factor, comprising the following steps:

[0029] Step 1, establish a second-order RC equivalent circuit model, the expression of the equivalent circuit model is

[0030]

[0031] After discretizing the expression, the Laplace equation of the equivalent circuit model is discretized to formula (1), and the state equation is solved as follows:

[0032]

[0033]

[0034] in:

[0035] From equations (2) (3), the Laplace equation of the battery model can be obtained as follows.

[0036]

[0037]

[0038] Step 2, discretize the Laplace equation (6) of the equivalent circuit model in step 1 by bilinear transformation, so that The discretized transfer function can be obtained:

[0039]

[0040] The differential equation of the eq...

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Abstract

The invention discloses a lithium battery model parameter recognition method through a least square method with a forgetting factor, and the recognition method comprises the steps: 1, building a second-order RC equivalent circuit model; 2, carrying out the bilinear transformation, and obtaining a difference equation of the system input and output of the equivalent circuit model; 3, building a recursive least square method with the forgetting factor (shown in the description); 4, building a coefficient equation; 5, collecting the parameters of a battery; 6, recognizing the parameters of the equivalent circuit model. According to the invention, the OCV-SOC function relation and the least square method with the forgetting factor are employed for the dynamic parameter recognition of the second-order RC equivalent circuit model, and the forgetting factor is introduced to a normally used recursive least square method. The method solves a problem of old data overlapping in an operation recursion process, and avoids the phenomenon of data saturation.

Description

technical field [0001] The invention relates to the field of batteries, in particular to a method for identifying parameters of a lithium battery model with a least squares method with a forgetting factor. Background technique [0002] The internal chemical reactions in the charge and discharge process of lithium-ion batteries are relatively complex, so this process is time-varying and nonlinear, so it is very difficult to obtain the parameters of the model through theoretical analysis. Although the model parameters are identified by the offline exponential fitting method at present, due to the time-varying nature of the battery system, the model parameters will also change greatly with the changes of battery SOC, external temperature, cycle times and other factors, so In order to improve the estimation accuracy of SOC and enhance the adaptability of the system, it is necessary to identify the battery model parameters online and make real-time corrections. Parameter estimat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
CPCG01R31/367
Inventor 刘厚德康龙云卢楚生饶华兵辛创张诚
Owner DONGGUAN DRN NEW ENERGY
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