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Lithium ion battery remaining life direct prediction method based on probability integration

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problem of lack of remaining life uncertainty, instability, etc., achieve scientific maintenance decision-making reference, overcome instability. Effect

Inactive Publication Date: 2014-07-30
HARBIN INST OF TECH
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Problems solved by technology

[0046] The purpose of the present invention is to provide a method for directly predicting the remaining life of a lithium-ion battery based on probability integration in order to solve the instability of the traditional monotone echo state network (MONESN) method and the lack of uncertainty expression of the remaining life

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  • Lithium ion battery remaining life direct prediction method based on probability integration
  • Lithium ion battery remaining life direct prediction method based on probability integration
  • Lithium ion battery remaining life direct prediction method based on probability integration

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specific Embodiment approach 1

[0065] Specific implementation mode one: combine figure 1 To illustrate this embodiment, the method for directly predicting the remaining life of a lithium-ion battery based on probability integration described in this embodiment includes the following steps:

[0066] Step 1. Measure the lithium-ion battery capacity to obtain the lithium-ion battery capacity data sequence;

[0067] Step 2, using the lithium-ion battery capacity data sequence in step 1, using N monotone echo state network models MONESN to predict the remaining life of lithium-ion batteries, and obtaining the prediction results of the remaining life of N lithium-ion batteries; N is a positive integer;

[0068] Step 3: Estimating the uncertainty interval of the remaining life prediction result of the lithium-ion battery, and obtaining the prediction result of the remaining life of the lithium-ion battery based on probability integration.

[0069] In this embodiment, N monotone echo state models are used to predi...

specific Embodiment approach 2

[0077] Specific implementation mode two: combination figure 2 Describe this embodiment. This embodiment is a further limitation of the method for directly predicting the remaining life of a lithium-ion battery based on probability integration described in Embodiment 1. In this embodiment, the prediction result of the remaining life of a lithium-ion battery described in step 3 The method to estimate the uncertainty interval is as follows:

[0078] The remaining life prediction results of N lithium-ion batteries in step 2 are output as a sub-model, and the output data of the sub-model obeys the Weibull distribution, and its probability density function is:

[0079] g ( f ) = β η β f β - 1 e - ( ...

specific Embodiment approach 3

[0085] Specific implementation mode three: combination image 3 and Figure 4 This embodiment is described. This embodiment verifies the method for directly predicting the remaining life of a lithium-ion battery based on probabilistic integration described in Embodiments 1 and 2.

[0086] Li-ion battery dataset:

[0087] Two types of Li-ion battery datasets are used for method validation. The operation and test conditions of the two types of data sets are different, including different types of battery samples to ensure the validity of the method verification.

[0088] The first type of data set comes from the NASA PCoE laboratory. The test object sample is a commercial 18650 lithium-ion battery with a rated capacity of 2Ah. The battery experiments (charging, discharging and impedance measurement) are run at room temperature (25°C). 1) Charge in a constant current mode of 1.5A until the battery voltage reaches 4.2V; 2) Discharge in a constant current mode of 2A until the ba...

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Abstract

The invention provides a lithium ion battery remaining life direct 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 a traditional MONESN method is unstable and lack of remaining life uncertainty expression. The method comprises the steps of firstly measuring the maximum capacity of a lithium ion battery in easy circulating period; adopting N MONESN models to predict the lithium ion battery remaining life and obtain N prediction results; performing uncertainty estimation and integration on the prediction results so as to obtain a lithium ion battery remaining life prediction result based on probability integration. The lithium ion battery remaining life direct prediction method fully plays the strong non-linear prediction capacity of the MONESN models and effectively solves the problem of instability of a traditional MONESN algorithm. In addition, uncertainty expression and management are achieved. The lithium ion battery remaining life direct prediction method is suitable for lithium ion battery remaining life prediction under the condition that the capacity can be directly measured and obtained.

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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IPC IPC(8): G01R31/36
Inventor 刘大同彭宇周建宝卢斯远彭喜元
Owner HARBIN INST OF TECH
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