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An RLS lithium battery model parameter online identification method based on a variable forgetting factor

A technology of forgetting factors and model parameters, which is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of slow convergence speed and low identification accuracy of time-varying systems, and achieve good tracking and identification parameters, and good identification Good performance and convergence

Inactive Publication Date: 2019-05-07
WUHAN UNIV OF TECH
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Problems solved by technology

[0006] For time-varying systems, the convergence speed is slow and the identification accuracy is low

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  • An RLS lithium battery model parameter online identification method based on a variable forgetting factor
  • An RLS lithium battery model parameter online identification method based on a variable forgetting factor
  • An RLS lithium battery model parameter online identification method based on a variable forgetting factor

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[0061] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0062] In the prior art, the requirement of a high-precision SOC cannot be met. The amount of calculation is large, and it is not suitable for battery management systems. For time-varying systems, the convergence speed is slow and the identification accuracy is low.

[0063] In order to solve the above problems, the present invention will be described in detail below in conjunction with specific solutions.

[0064] like Figure 1-Figure 2 As shown, the online identification method of the RLS lithium battery model parameters based on the variable forgetting factor provided by the embodiment of the present invention includ...

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Abstract

The invention belongs to the field of lithium battery model identification, and discloses an RLS lithium battery model parameter online identification method based on a variable forgetting factor, which comprises the following steps: acquiring lithium battery performance parameter information; Establishing a second-order lithium battery equivalent circuit model, and deducing a model identificationvector equation; Obtaining the OCV-SOC characteristic curve of the battery at different temperatures, and fitting the relationship expression of OCV-SOC; Calculating parameters of the model equationat the current moment according to an RLS method of the variable forgetting factor; Updating the forgetting factor value at the next moment; And collecting voltage and current values at the next moment, and enabling the second-order RC equivalent circuit model to be subjected to online parameter identification at the next moment by utilizing an updated forgetting factor RLS method. The method canquickly converge to a real value; And for time-varying lithium battery model parameters, compared with an RLS method of a fixed forgetting factor, the method has the on-line tracking capability of higher convergence rate and higher identification precision.

Description

technical field [0001] The invention belongs to the field of lithium battery model identification, in particular to an online identification method for RLS lithium battery model parameters based on a variable forgetting factor. Background technique [0002] At present, the domestic and foreign lithium battery model parameter identification mainly adopts the offline identification method, that is, the pulse method is used to obtain the response of the lithium battery to the pulse, and the battery model parameters are identified according to the obtained offline data. However, the lithium battery model parameters are a time-varying system, which changes with the change of SOC, and the parameter results of offline identification cannot meet the requirements of high-precision SOC. In recent years, three methods for online identification of lithium battery model parameters have emerged at home and abroad, including: least squares method (RLS), genetic algorithm (GA), and swarm pa...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 徐劲力徐维许建宁颜晓凤谢锋
Owner WUHAN UNIV OF TECH
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