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Estimation method of SOC of lithium battery

A state of charge, lithium battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., to achieve accurate prediction results

Inactive Publication Date: 2019-08-02
ZHEJIANG UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] Object of the present invention overcomes the deficiency of existing lithium battery state of charge prediction method

Method used

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  • Estimation method of SOC of lithium battery
  • Estimation method of SOC of lithium battery
  • Estimation method of SOC of lithium battery

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Embodiment Construction

[0033] In order that those skilled in the art can better understand the technical solution of the present invention, its specific implementation will be described in detail below in conjunction with the accompanying drawings:

[0034] see Figure 1 to Figure 3 , the preferred embodiment of the present invention,

[0035] A lithium battery state of charge estimation method is characterized in that, comprising the following steps:

[0036] S1, the original data set input step, input the original data set containing the working data of the lithium battery;

[0037] S2, the data cleaning step, extracting the required data fields. Remove null and negative values ​​in the data field. Then calculate the remaining amount of lithium battery charge corresponding to each set of data as the data label. Then generate a standard two-dimensional data matrix;

[0038] S3, set the initial value of the genetic algorithm, the population size is 40, the number of iterations is 50, and the en...

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Abstract

The invention discloses an estimation method of SOC of a lithium battery. The method includes following steps: step 1, inputting an original data set; step 2, cleaning the original data set, and extracting a required data field; step 3, setting an initial value of a genetic algorithm, and selecting adoption of a mean square error of a nonlinear autoregression external source input neural network prediction result to be regarded as a fitness function of the genetic algorithm; step 4, establishing a neural network; step 5, optimizing the genetic algorithm; and step 6, training a prediction model. According to the method, the genetic algorithm and the non-linear autoregression external source input neural network are combined together so that the workload and the time for searching the optimal neural network parameter can be reduced, the SOC of the lithium battery is accurately predicted, and the health condition of the battery can be reflected.

Description

technical field [0001] The present invention relates to lithium battery technology and the field of artificial neural network technology, and provides a method for estimating the state of charge of a lithium battery, in particular to a method for estimating the state of charge of a lithium battery based on a nonlinear autoregressive external source input neural network. Background technique [0002] The state of charge (SOC) is an important indicator of the remaining power in a Li-ion battery. is calculated based on the integral of the current. Accurate and robust SOC estimation technology can avoid overcharge, overdischarge and overheating, thereby prolonging the service life of the battery. The existing state of charge estimation method has the defect of relying on the accuracy of the battery model or inaccurate estimation results. Battery aging leads to charging The reduced capacity also adds to the difficulty of accurately estimating the state of charge with existing te...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/392
Inventor 郭创新朱承治袁根王雪平曹袖
Owner ZHEJIANG UNIV
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