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Online lithium ion battery residual life predicting method based on relevance vector regression

A lithium-ion battery and correlation vector technology, applied in the field of lithium-ion battery life prediction, can solve problems such as low prediction accuracy

Inactive Publication Date: 2013-03-13
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The present invention is to solve the problem that the existing lithium-ion battery adopts an off-line method to predict the remaining life and the prediction accuracy is low, and provides an online method for predicting the remaining life of the lithium-ion battery based on correlation vector regression

Method used

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  • Online lithium ion battery residual life predicting method based on relevance vector regression
  • Online lithium ion battery residual life predicting method based on relevance vector regression
  • Online lithium ion battery residual life predicting method based on relevance vector regression

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specific Embodiment approach 1

[0044] Specific implementation mode 1: the following combination figure 1 To explain this embodiment, the method for online prediction of the remaining life of a lithium-ion battery based on correlation vector regression in this embodiment includes the following steps:

[0045] Step 1: Select the capacity data IS=(C 1 , C 2 ,...C n ) As the original sample, C i Is the battery capacity, the unit is Ah, i=1, 2,..., n, n is a positive integer;

[0046] Perform phase space reconstruction to construct a training sample set: set the embedding dimension l=5 and delay d=1 to obtain the training sample set {(x 1 , Y 1 ), (x 2 , Y 2 ),..., (x n-l , Y n-l )}, where x j =(C j , C j+1 ,..., C j+l-1 ), y j =C j+l , J = 1, 2, ... n-l, where x = (x 1 , X 2 ,..., x n-l ) Is the input data for the relevance vector machine RVM model, y=(y 1 , Y 2 ,..., y n-l ) Is the output data of the correlation vector machine RVM model;

[0047] Step 2: Initialize the RVM model parameters of the correlation vector m...

specific Embodiment approach 2

[0076] Specific embodiment two: this embodiment is a further description of the first embodiment, and the prediction error limit in this embodiment is PEB=0.1.

specific Embodiment approach 3

[0077] Specific implementation manner three: the following combination Figure 1 to Figure 3 This embodiment is described. This embodiment is a further explanation of the first or second embodiment. In this embodiment, the failure threshold U=1.38Ah.

[0078] Most of the driven lithium-ion battery remaining life prediction methods are offline, with low dynamic update ability and low prediction accuracy, and the calculation efficiency of online retraining is low, especially the correlation vector machine algorithm is directly used for online modeling And forecast. At the same time, due to the sparsity of the relevance vector machine algorithm and the challenges in the long-term prediction of the remaining life, it is difficult for the offline relevance vector machine algorithm to obtain accurate prediction results. Therefore, the update of the sample, the retraining of the model, and the dynamic characteristics of online update with the sample are very important in the remaining l...

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Abstract

The invention discloses an online lithium ion battery residual life predicting method based on relevance vector regression, belongs to the technical field of lithium ion battery life prediction, and solves the problem that the residual life of the existing lithium ion battery is predicted by an offline method with low precision. The method comprises the following steps: firstly selecting original samples, performing phase-space reconstruction to construct a training sample set; initializing the model parameters of RVM (relevance vector machine); performing RVM training to obtain a RVM prediction model; comparing the obtained prediction value with ynew, if yes, the constructed novel training set WS equal to WSUINS, retraining RVM, and updating the RVM prediction model; otherwise, keeping the RVM prediction model stable; performing recurrence prediction until the prediction value is smaller than the invalid threshold value U, and finishing the online prediction of the residual life of the predicted lithium ion battery. The method is suitable for prediction of the lithium ion battery residual life.

Description

Technical field [0001] The invention relates to a method for online prediction of the remaining life of a lithium ion battery based on correlation vector regression, and belongs to the technical field of lithium ion battery life prediction. Background technique [0002] Lithium-ion batteries have been used in various fields of our lives with their superior performance, and have gradually expanded to aviation, aerospace and other fields, such as on-orbit satellites and space stations. As the charge-discharge cycle progresses, the internal resistance of the lithium-ion battery increases and the 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 Mars Global Surveyor aircraft in the United States is caused by a series of errors in the computer system due to battery failure, which caused the battery system to face the sun and cause overheating....

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 周建宝刘大同马云彤彭宇彭喜元
Owner HARBIN INST OF TECH
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