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Indirect prediction method for remaining life of lithium ion battery based on probabilistic integration

A lithium-ion battery life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of unmeasurable and unstable lithium-ion battery capacity

Active Publication Date: 2017-02-01
HARBIN INST OF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0046] The purpose of the present invention is to solve the problem that the lithium-ion battery capacity cannot be measured under online working conditions and the instability of the traditional monotone echo state network (MONESN) method, and provide a method for indirect prediction of the remaining life of lithium-ion batteries based on probability integration

Method used

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  • Indirect prediction method for remaining life of lithium ion battery based on probabilistic integration
  • Indirect prediction method for remaining life of lithium ion battery based on probabilistic integration
  • Indirect prediction method for remaining life of lithium ion battery based on probabilistic integration

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specific Embodiment approach 1

[0076] Embodiment 1: Combining figure 2 and Figure 4 To illustrate this embodiment, the probabilistic integration-based indirect prediction method for the remaining life of lithium-ion batteries described in this embodiment includes the following steps:

[0077] Step 1, monitor the discharge time interval and discharge voltage during the discharge process of the lithium-ion battery, construct a time interval sequence of equal discharge voltage differences according to the discharge time interval and discharge voltage, and use it as the health factor HI for remaining life prediction;

[0078] Step 2, using the generalized linear regression model GLRM to describe the correlation between the HI constructed in step 1 and the lithium-ion battery capacity, the GLRM is the HI correlation model;

[0079] Step 3, using the GLRM in step 2 to estimate the failure threshold of HI, the failure threshold corresponds to the capacity;

[0080] Step 4, using the failure threshold in step 3 a...

specific Embodiment approach 2

[0097] Specific implementation mode two: combination image 3 Describe this embodiment. This embodiment is a further limitation of the probabilistic integration-based indirect prediction method for the remaining life of lithium-ion batteries described in Embodiment 1. In this embodiment, the method for constructing HI in step 1 is: Charge and discharge once, which is defined as a cycle. For all cycles, select a discharge voltage with a common interval. The maximum value of the common interval is Vmax, and the minimum value is Vmin. Calculate the voltage corresponding to this voltage interval in each cycle. time interval, the time interval is h i , the h of different cycles i Permutation, to obtain a sequence with a degradation trend and can reflect the degradation of battery life, the sequence is HI.

[0098] like image 3 As shown, after selecting Vmax and the minimum value of Vmin, for each cycle, record the time t corresponding to Vmax and Vmin respectively Vmax and t ...

specific Embodiment approach 3

[0099] Specific Embodiment Three: This embodiment is a further limitation of the probabilistic integration-based indirect prediction method for the remaining life of lithium-ion batteries described in Embodiment 1. In this embodiment, the HI correlation model GLRM in step 2 is expressed as:

[0100] c i = β 0 +β 1 h i +β 2 ln(h i )+ε i

[0101] c i is the battery capacity of the i-th charge-discharge cycle, h i is the i-th element in the time interval sequence based on the equal discharge voltage difference, β 1 and beta 2 is the coefficient of the regression model, β 0 is a constant, ε i is the error term.

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Abstract

The invention provides a lithium ion battery remaining life indirect prediction method based on probability integration, relates to the technical field of lithium ion battery remaining life prediction and aims at solving the problem that under the online working condition, the capacity of a lithium ion battery is immeasurable and a traditional MONESN method is unstable. The method comprises the steps of firstly establishing a health factor HI; establishing an HI relevancy model GLRM according to the HI; utilizing GLRM to estimate an HI invalidation threshold value; utilizing the invalidation threshold value to perform the lithium ion battery remaining life prediction; performing uncertainty expression on a prediction result. The lithium ion battery remaining life indirect prediction method overcomes the shortcoming that most of lithium ion battery remaining life prediction depends on maximum capacity, solves the problem that the invalidation threshold value of the established HI serves as a judgment condition of end of life and is difficult to confirm and effectively solves the problem of instability of a traditional MONESN method. In addition, uncertainty expression and management are achieved. The lithium ion battery remaining life indirect prediction method is suitable for lithium ion battery remaining life prediction when the capacity is immeasurable.

Description

technical field [0001] The invention relates to the technical field of remaining useful life (Remaining Useful Life, RUL) prediction of lithium ion batteries. Background technique [0002] Compared with traditional NiMH batteries and NiCd batteries, lithium-ion batteries have many advantages, such as high energy density, long life, high output voltage, low self-discharge rate, high reliability and safety, etc. Therefore, lithium-ion batteries are widely used in electric vehicles, consumer electronics, communications, navigation, navigation, aviation, aerospace and other fields. In particular, lithium-ion batteries have become the third-generation satellite batteries, which can effectively improve load efficiency and reduce spacecraft self-respect. [0003] With the rapid development of lithium-ion battery technology and its rapid promotion in many industrial fields, battery performance degradation, prediction and life prediction, maintenance optimization, etc., have attract...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/36
Inventor 刘大同彭宇庞景月贺思捷彭喜元
Owner HARBIN INST OF TECH
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