Battery life prediction method, system and device based on model migration

A battery life and prediction method technology, applied in the direction of prediction, electrical digital data processing, special data processing applications, etc., can solve the problems of high time cost, low accuracy of prediction results, and the inability to consider the actual aging time of batteries, etc., to reduce the time Cost, effect of improving accuracy

Inactive Publication Date: 2019-01-04
GUANGZHOU HKUST FOK YING TUNG RES INST
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Problems solved by technology

[0002] With the development of battery technology, the life of the battery has reached tens of thousands of cycles, resulting in the existing method of using actual working conditions to verify the reliability of the battery takes several years, and the time cost is high; When using the Yas

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  • Battery life prediction method, system and device based on model migration
  • Battery life prediction method, system and device based on model migration
  • Battery life prediction method, system and device based on model migration

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Embodiment Construction

[0045] The present invention will be further explained and described below in conjunction with the accompanying drawings and specific embodiments of the description. For the step numbers in the embodiment of the present invention, it is only set for the convenience of explanation and description, and there is no limitation on the order of the steps. The execution order of each step in the embodiment can be carried out according to the understanding of those skilled in the art Adaptive adjustment.

[0046] refer to figure 1 , the battery life prediction method based on model migration of the present invention, comprises the following steps:

[0047] According to the experimental data in the battery accelerated aging experiment process, the battery accelerated aging model is established;

[0048] According to the battery accelerated aging model and the experimental data in the battery slow aging process, the Bayesian Monte Carlo method is used to establish the battery slow agi...

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Abstract

The invention discloses a battery life prediction method, a system and a device based on model migration. The method comprises the following steps: establishing a battery accelerated aging model according to the experimental data in the accelerated aging test process of the battery; Bayesian Monte Carlo method was used to establish the battery slow aging model according to the accelerated aging model and the experimental data of the battery slow aging process. Input battery aging time to predict the battery life; the system includes battery accelerated aging model building module, battery slowaging model building module and prediction module. The apparatus includes a memory and a processor. The invention reduces the time cost of battery life prediction, improves the accuracy of predictionresult, and can be widely applied in the battery testing technical field.

Description

technical field [0001] The invention relates to the technical field of battery testing, in particular to a battery life prediction method, system and device based on model migration. Background technique [0002] With the development of battery technology, the life of the battery has reached tens of thousands of cycles, resulting in the existing method of using actual working conditions to verify the reliability of the battery takes several years, and the time cost is high; When using the Yass method to predict battery life, it must first assume that the battery aging trend under the tested working conditions and future working conditions is the same, and then directly predict the battery life, which cannot consider the important factor of battery actual aging time. The accuracy is low. Contents of the invention [0003] In order to solve the above-mentioned technical problems, the object of the present invention is to provide a fast and highly accurate battery life predi...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06F17/50
CPCG06Q10/04G06F30/20
Inventor 唐晓鹏姚科夏永晓胡文贵贺振伟高福荣
Owner GUANGZHOU HKUST FOK YING TUNG RES INST
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