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Lithium battery SOH estimation and RUL prediction method based on AST-LSTM neural network

A technology of neural network and prediction method, applied in the field of lithium battery PHM

Active Publication Date: 2020-02-21
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] Based on the defects of the above method, the present invention proposes a new active state tracking long-short-term memory (ActiveStates Tracking long-short-term memory, AST-LSTM) neural network model to solve the lithium battery SOH estimation and RUL prediction problems

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  • Lithium battery SOH estimation and RUL prediction method based on AST-LSTM neural network
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  • Lithium battery SOH estimation and RUL prediction method based on AST-LSTM neural network

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[0090] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0091] see Figure 1 ~ Figure 3 , is a lithium battery SOH estimation and RUL prediction method based on AST-LSTM neural network, including the following ste...

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Abstract

The invention relates to a lithium battery SOH estimation and RUL prediction method based on an AST-LSTM neural network, and belongs to the field of lithium battery PHM. The method comprises the following steps: 1) collecting voltages, currents, temperatures and corresponding capacity values of multiple battery charging and discharging periods; 2) constructing a deep AST-LASTM model; and 3) carrying out lithium battery SOH estimation and RUL prediction on the basis of the AST-LSTM neural network. The battery capacity data can be obtained by only measuring voltage, current, temperature and timeof a lithium battery to be detected, the SOH and RUL of the lithium battery are estimated, the measurement process is simple, the error is low, and the precision is high.

Description

technical field [0001] The invention belongs to the field of lithium battery PHM, and relates to a lithium battery SOH estimation and RUL prediction method based on AST-LSTM neural network. Background technique [0002] As a lightweight, high-density energy storage and supply device, lithium batteries are key enabling components for many complex electric drive systems (such as spacecraft, electric vehicles, and portable electronic devices). The safe and stable operation of lithium batteries affects the reliable operation of various equipment in its application fields. Therefore, research on Prognostic and Health Management (PHM) of lithium batteries has always been the focus of academic and engineering fields. Lithium battery PHM mainly involves real-time estimation of SOH, real-time prediction of RUL and monitoring of other battery parameters within a certain charge-discharge cycle. The estimation of SOH and the prediction of RUL are mainly aimed at the service life of th...

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Application Information

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IPC IPC(8): G01R31/367G01R31/392G06N3/04G06N3/08
CPCG01R31/367G01R31/392G06N3/084G06N3/048G06N3/044G06N3/045
Inventor 李鹏华张家昌张子健柴毅熊庆宇丁宝苍魏善碧
Owner CHONGQING UNIV OF POSTS & TELECOMM
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