Method and device for predicting life of satellite lithium ion battery

A technology of lithium-ion batteries and lithium batteries, which can be used in measuring devices, measuring electricity, and measuring electrical variables, etc., can solve the problems of large errors in lithium-ion life

Inactive Publication Date: 2016-07-27
BEIJING AEROSPACE MEASUREMENT & CONTROL TECH
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  • Abstract
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Problems solved by technology

[0004] The invention provides a method and device for predicting the service life of a satellite lithium-ion battery, which is used to solve the problem that in the prior art, there are many inaccuracies in the process of measur...

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  • Method and device for predicting life of satellite lithium ion battery
  • Method and device for predicting life of satellite lithium ion battery
  • Method and device for predicting life of satellite lithium ion battery

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Embodiment 1

[0082] The embodiment of the present invention provides how to use LSSVM to build a cycle life prediction model, see Figure 5 , the specific implementation steps are as follows.

[0083] Step 1: Select appropriate degraded data as training samples. Based on the superior performance of radial basis function in LSSVM regression prediction application, radial basis function is selected as the LSSVM kernel function of this scheme.

[0084] The second step: initialize the model, that is, use the kernel width σ and penalty factor γ to initialize the radial basis kernel function.

[0085] Step 3: Use the LSSVM cross-validation function tunelssvm to optimize and solve the kernel width σ and penalty factor γ.

[0086] Step 4: Substitute the kernel width σ and penalty coefficient γ into the LSSVM training model, use the trainlssvm command to solve the model parameters and b value, and then train and build the model.

[0087] Step 5: Validate the model. The precision of the model was...

Embodiment 2

[0093] The embodiment of the present invention is a verification of the lithium-ion battery cycle life prediction model established based on the degradation data and LSSVM. In this embodiment, the lithium-ion battery test data of NASAP CoE Research Center is used for verification, and the test verification results are compared and analyzed. Select #05, #06 and #07 battery capacity decline test data in the test data set, and take 70% battery capacity (from 2Ah to about 1.4Ah) as the threshold of lithium-ion battery life.

[0094] First, the measurement of battery capacity (EIS) will cause a relaxation effect on the capacity, such as Image 6 The peak of the curve shown is the relaxation effect, and the method based on the mathematical form is used to remove the relaxation effect on the capacity data, such as Figure 7 shown.

[0095] Then, the kernel function and model parameters of the LSSVM model are reasonably selected. The expression of the LSSVM model is: Among them, ...

Embodiment 3

[0102] According to the embodiment of the present invention, on the basis of establishing model 1 and model 2 using the degradation data of the first 40 cycles and the first 70 cycles of #5 and #6 respectively, the corresponding model 1 is used to predict the 60 cycle and model backward respectively. 2 Forecast 30 cycles backward, and then perform life prediction based on the threshold value (70%) of battery capacity.

[0103] Utilize the SIMLSSVM command in the LSSVM algorithm for RUL prediction. The difference between the predicted life and the actual life is the prediction error, which is also expressed by the mean square value (MSE) and the square correlation coefficient (SCC), and the relative error (RPE) is also added to linearly represent the error, as shown in formula (11) Show.

[0104] RPE = | C r - C p ...

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Abstract

The invention discloses a method and device for predicting the life of a satellite lithium ion battery. The method comprises the following steps: analyzing the characteristics of cycle life test data of the satellite lithium ion battery so as to obtain a fault evolution characteristic quantity; carrying out de-relax effect on capacity degradation data corresponding to the fault evolution characteristic quantity; carrying out least squares support vector machine LSSVM unsupervised learning training according to the capacity degradation data after the de-relax effect so as to complete the construction of an LSSVM model for life prediction; predicting the battery capacities of the battery in different periods through the LSSVM model, and carrying out battery failure threshold value extrapolation according to the battery capacities so as to realize the real-time prediction of the remaining useful life RUL of the lithium battery. According to the method provided by the invention, the LSSVM model for life prediction is constructed after de-relax effect is carried out on the capacity degradation data, and then life prediction is carried out through the model, so that the prediction result is correct and the problem that the process of measuring the life of the lithium ion battery is not correct is solved.

Description

technical field [0001] The invention relates to the field of lithium ion batteries, in particular to a method and device for predicting the service life of satellite lithium ion batteries. Background technique [0002] As a new type of battery, lithium-ion batteries have greater advantages than previous types of batteries. They are used in occasions that require high electrical performance and reliability of energy storage power sources, such as low-earth orbit satellites (LEO), geosynchronous orbit satellites (GEO), space stations, etc. For aerospace equipment, lithium-ion battery packs will become the first choice. [0003] However, in the prior art, there are many inaccuracies in the process of measuring the life of lithium-ion batteries, resulting in large errors in the obtained life of lithium-ion batteries, which affects the accurate evaluation of the life of lithium-ion batteries. Contents of the invention [0004] The invention provides a method and device for pre...

Claims

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Application Information

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IPC IPC(8): G06F19/00G01R31/36
Inventor 樊焕贞房红征罗凯李蕊
Owner BEIJING AEROSPACE MEASUREMENT & CONTROL TECH
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