Lithium ion battery remaining life prediction method based on grey particle filter

A lithium-ion battery and particle filtering technology, which is applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of small data volume, increased battery life data volume, time-varying, etc., to achieve good forecast performance and meet forecast requirements. Effect

Active Publication Date: 2018-04-17
GUANGXI UNIV
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Problems solved by technology

[0003] At present, in the remaining life prediction of lithium-ion batteries, there are three problems in establishing a high-precision and adaptable battery life prediction method framework: (1) The amount of data is small: the amount of data representing battery life (such as the capacity of lithium-ion batteries) Increases as batteries age, so less early in forecast
(2) Complic

Method used

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  • Lithium ion battery remaining life prediction method based on grey particle filter
  • Lithium ion battery remaining life prediction method based on grey particle filter
  • Lithium ion battery remaining life prediction method based on grey particle filter

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Embodiment Construction

[0032] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0033] The present invention is based on gray particle filtering lithium-ion battery remaining life prediction method, such as figure 1 The following steps are shown:

[0034] Step 1, establish a lithium-ion battery cycle life degradation model:

[0035] Step 1-1, extract the battery capacity data required for prediction, perform preprocessing and eliminate outlier data as sample data S; step 1-2, use sample data S as the input data sequence of the gray prediction model, and calculate lithium-ion battery The gray development coefficient a of capacity decay is calculated as follows:

[0036] Step ①, the battery real capacity value data in the sample data S Form sequence X (0) , then X (0) Can be expressed as:

[0037] Step ②, for sequence X (0) Perform 1-AGO (1-accumulating generation operation) transformation to obtain sequence X (1) , then ...

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Abstract

The invention relates to a lithium ion battery remaining life prediction method based on grey particle filter, which can use a small data amount for aging modeling, accurately estimate the battery capacity state and improve the battery life prediction accuracy. The method comprises steps: battery capacity data which are needed for prediction are firstly extracted as sample data to be inputted to agrey prediction model, the grey evolution coefficient a for capacity degradation of the lithium ion battery is calculated, and thus, a state transfer equation and an observation equation for the lithium ion battery capacity during the aging process are built; a particle filter algorithm is then used to track and update the battery capacity state changes; and finally, when prediction begins, according to the grey evolution coefficient, the change value of the capacity of each particle along with the cycle number is extrapolated, and according to the weight of each particle, the remaining lifeof the battery is predicted, and probability density distribution is given.

Description

Technical field: [0001] The invention belongs to the technical field of lithium-ion batteries, and more specifically relates to a method for predicting the remaining life of lithium-ion batteries. Background technique: [0002] In the face of global energy and environmental crises, lithium-ion batteries have been widely used as the main energy storage equipment in the automotive industry, aerospace, and grid energy storage. The normal operation and status monitoring of lithium-ion batteries are the guarantee of system stability, and the correct prediction of battery remaining life is a key factor to realize battery health management, which can greatly reduce the probability of system failure. The remaining life of lithium-ion batteries is also called cycle life, which refers to the number of charge and discharge cycles experienced when the battery degrades from the rated capacity to the point where it cannot maintain the equipment or degrades to less than 70% or 80% of the r...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367G01R31/392
Inventor 陈琳王峥峥韦海燕潘海鸿
Owner GUANGXI UNIV
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