Health state and residual life prediction method of multi-type lithium ion battery pack management system

A technology of lithium-ion battery packs and lithium-ion batteries, which is applied in the direction of measuring electricity, measuring devices, and measuring electrical variables, and can solve problems such as poor prediction accuracy and difficult battery balance management

Active Publication Date: 2020-09-25
ZHONGBEI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims to solve the problem that in the use of the battery management system when multiple types of lithium-ion batteries are mixed, the health state prediction and

Method used

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  • Health state and residual life prediction method of multi-type lithium ion battery pack management system
  • Health state and residual life prediction method of multi-type lithium ion battery pack management system
  • Health state and residual life prediction method of multi-type lithium ion battery pack management system

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specific Embodiment approach 1

[0040] Specific embodiment one: figure 1 This embodiment is a simplified structural diagram of a multi-type lithium-ion battery pack management system in an actual application scenario. The system consists of multiple independent lithium-ion battery management systems, and each system can use different types of lithium-ion batteries. Each independent system is independently responsible for completing its own balance management, temperature warning and SOH prediction functions. Such a management structure can ensure that when a certain part fails, it will not affect the overall operation.

[0041] figure 2 Schematic diagram of the composition of the lithium-ion battery management system. Each management system consists of three modules (not including battery), which are the controller module, the sensor module, and the memory and information transmission module.

[0042] The controller module consists of an STM32 chip, a charge and discharge power controller, and a cooling fan. T...

specific Embodiment approach 2

[0051] Embodiment 2: This embodiment further explains the method for predicting health and remaining life based on the reconfigurable lithium-ion battery management system described in Embodiment 1. The steps of independent principal component analysis described in step 1 are as follows :

[0052] Record the capacity degradation data of the lithium-ion battery pack as: (Abbreviated as C), where n is the number of the single battery of the same type, and m is the number of cycles of the single battery.

[0053] Step 1. Subtract C from its mean

[0054] Step 2: Calculate R=EDE T , Where R is the covariance matrix of C, E is the orthogonal matrix of eigenvectors, and D is the diagonal matrix of its eigenvalues;

[0055] Step 3: Calculate the whitening data C v =ED -1 / 2 E T x;

[0056] Step 4: Set the number of iterations p, and initialize the vector W randomly p , So that the sum of each row is 1. Then according to the formula

[0057]

[0058] Find W at time i i . Where g'(.) is the s...

specific Embodiment approach 3

[0061] Specific embodiment three: This embodiment is to further explain the method for predicting the state of health and remaining life of the multi-type lithium-ion battery pack management system described in the first embodiment. Figure 4 It is a flow chart of establishing a multi-scale combination model of single battery in the present invention. The specific process of establishing a multi-scale combined model described in step 2 and step 3 to realize the prediction of lithium-ion pre-health state and remaining life is as follows:

[0062] Step 1. Use discrete wavelet decomposition to decompose the monomer capacity degradation data into two parts: high frequency fluctuation and low frequency trend;

[0063] Step 2: Bring the high-frequency fluctuation part and low-frequency trend part of the same battery (except the test data) obtained in step 1 into the residual wavelet neural network for training;

[0064] Step 3: Combine the residual wavelet network of the trained low-freque...

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Abstract

The invention discloses a health state and residual life prediction method of a multi-type lithium ion battery pack management system, and aims to solve the problems that under the condition that multiple types of lithium ion batteries are used in a mixed mode, a traditional battery management system cannot achieve effective management, and the prediction starting point of a traditional predictionmodel is relatively backward. According to the method, wavelet decomposition is adopted for lithium ion batteries of the same type, capacity degradation historical data are divided into a high-frequency fluctuation part and a low-frequency trend part, and the two parts of data serve as input data to train wavelet neural networks with residual layers corresponding to the two parts of data; real-time low-frequency trend data is substituted into a residual wavelet network and unscented particle filter combined model to obtain a long-term residual life prediction result, wherein the result provides a basis for the later battery replacement sequence of the system; and the residual life prediction result and a short-term prediction value obtained by using a wavelet neural network model with a residual layer in a real-time high-frequency fluctuation part are superposed through the same number of cycles to obtain a real-time health state prediction value for lithium ion battery health state balance management.

Description

Technical field [0001] The invention belongs to the technical field of lithium ion battery health management, and specifically relates to a method for predicting the health status and remaining life of a multi-type lithium ion battery pack management system. Background technique [0002] Lithium-ion batteries have been widely used in industrial production and daily life. From electronic products such as mobile phones and laptops to large-scale application scenarios such as electric vehicles, artificial satellites and grid energy storage, lithium-ion batteries play an extremely important role. However, with the large-scale production of lithium-ion batteries, there are huge differences in batteries manufactured by different manufacturers. And there are also differences between different batches of the same manufacturer, and even the same batch of lithium-ion batteries. Therefore, the management of lithium-ion batteries when multiple types are mixed has become one of the difficul...

Claims

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Application Information

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IPC IPC(8): G01R31/392G01R31/367G01R31/396
CPCG01R31/392G01R31/367G01R31/396Y02E60/10
Inventor 贾建芳温杰王科科史元浩庞晓琼梁建宇曾建潮
Owner ZHONGBEI UNIV
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