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Lithium ion battery residual life forecasting method of dynamic gray related vector machine

A correlation vector machine, lithium-ion battery technology, applied in the measurement of electricity, measurement of electrical variables, measurement devices, etc., can solve problems such as poor prediction accuracy, and achieve the effect of improving accuracy, strong generalization ability, and fixing hyperparameters

Active Publication Date: 2013-04-10
哈尔滨诺信工大测控技术有限公司
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Problems solved by technology

[0006] In order to solve the shortcomings in the existing lithium-ion battery remaining life prediction method: the problem that only a single-point prediction value can be given and the prediction accuracy of the state transition equation is established by relying on an empirical model, the present invention proposes a dynamic gray correlation vector machine for lithium Method for Predicting the Remaining Life of Ion Batteries

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  • Lithium ion battery residual life forecasting method of dynamic gray related vector machine
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  • Lithium ion battery residual life forecasting method of dynamic gray related vector machine

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specific Embodiment approach 1

[0036] Specific implementation mode 1. Combination figure 1 Specifically illustrate the present embodiment, the lithium-ion battery remaining life prediction method of the dynamic gray correlation vector machine described in the present embodiment, comprises the following steps:

[0037] Step 1, select the lithium-ion battery capacity training data, and use this data set as the original data set;

[0038] Step 2, MDGM modeling, predicting the original data set;

[0039] Step 3. Update the elements in the original data set according to the prediction results;

[0040] Step 4. Determine whether the original data prediction is completed, if so, perform step 5; if not, repeat the prediction;

[0041] Step 5, according to the input is the predicted value set of the original data, the output is the original data set, and train the correlation vector machine model;

[0042] Step 6. Capacity prediction: perform short-term prediction based on the MDGM model obtained in step 2, and ...

specific Embodiment approach 2

[0060] Embodiment 2. The difference between this embodiment and the lithium-ion battery remaining life prediction method of the dynamic gray correlation vector machine described in Embodiment 1 is that the lithium-ion battery capacity training data is selected in step 1, and the data is The specific process of the collection as the original data collection is:

[0061] Select the lithium-ion battery capacity data set X of 60 charge-discharge cycles before the current moment (0) ={x (0) (1),x (0) (2),...x (0) (i)...,x (0) (n)} as the original data set,

[0062] Among them, n=60.

specific Embodiment approach 3

[0063] Specific embodiment three. The difference between this embodiment and the lithium-ion battery remaining life prediction method of the dynamic gray correlation vector machine described in specific embodiment two is that the MDGM modeling described in step two, the specific method of predicting the original data set The process is:

[0064] A. Establish the gray differential equation: use the selected original data set as the input data set of the MDGM(1,1) model, and obtain x according to the formula (1) (1) (k):

[0065] x ( 1 ) ( k ) = Σ i = 1 k x ( 0 ) ( i ) , - - - ...

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Abstract

The invention relates to a lithium ion battery residual life forecasting method of a dynamic gray related vector machine. The lithium ion battery residual life forecasting method of the dynamic gray related vector machine solves the problem of an existing lithium ion battery residual life forecasting method. The problem of poor accuracy of a state transition equation forecasting is solved only by giving single-point forecasting value and an empirical model. Firstly, a gray model is adopted to conduct trend forecasting on small sample data, then regression forecasting is conducted by a related vector machine, and finally the forecasting model is updated dynamically by related analysis, on the basis of a combined model, a short-term forecasting result is continuously updated into a training data series, the related analysis is conducted, and training is conducted again according to correlation, so that accuracy of multistep iteration forecasting is further improved. The lithium ion battery residual life forecasting method of the dynamic gray related vector machine is applied to the field of a lithium ion battery.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a lithium ion battery, in particular to a method for predicting the remaining life of a lithium ion battery using a dynamic gray correlation vector machine. Background technique [0002] Lithium-ion batteries have been used in various fields of our lives due to their superior performance, and have gradually expanded to aviation, aerospace and other fields, such as satellites in orbit and space stations. As the charge-discharge cycle progresses, the internal resistance of the lithium-ion battery increases and its lifespan decreases. For space applications that are inaccessible to humans, the failure or shortened life of lithium-ion batteries often leads to fatal failures. For example, the failure of the US Mars Global Surveyor aircraft is due to a series of errors in the computer system due to battery failure, which caused the battery system to directly face the sun and cause overhea...

Claims

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Application Information

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IPC IPC(8): G01R31/36
Inventor 彭宇刘大同周建宝郭力萌彭喜元
Owner 哈尔滨诺信工大测控技术有限公司
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