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GPR lithium battery health state prediction method based on neural network kernel function

A prediction method and health status technology, applied in the field of electrochemistry, can solve the problems of inaccurate prediction results and low prediction accuracy, and achieve the effects of low prediction uncertainty, high prediction accuracy and accurate prediction results

Inactive Publication Date: 2016-11-16
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0017] However, the current state-of-health prediction methods for lithium batteries have less accurate prediction results and lower prediction accuracy.

Method used

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  • GPR lithium battery health state prediction method based on neural network kernel function
  • GPR lithium battery health state prediction method based on neural network kernel function
  • GPR lithium battery health state prediction method based on neural network kernel function

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Embodiment Construction

[0042] Exemplary embodiments of the present invention will be described below with reference to the accompanying drawings. In the interest of clarity and conciseness, not all features of an actual implementation are described in this specification. It should be understood, however, that in developing any such practical embodiment, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as meeting those constraints related to the system and business, and those Restrictions may vary from implementation to implementation. Moreover, it should also be understood that development work, while potentially complex and time-consuming, would at least be a routine undertaking for those skilled in the art having the benefit of this disclosure.

[0043] Here, it should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only the device structure and / or processing steps closely related to the ...

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Abstract

The invention provides a GPR lithium battery health state prediction method based on a neural network kernel function. The method comprises the steps that a covariance function is determined based on the neural network kernel function to construct a GPR prediction model; a mean function in the GPR prediction model and hyperparameters in the covariance function are initialized; a logarithmic maximum likelihood estimation function is used to optimize the hyperparameters; and training data and test data are input into the GPR prediction model to acquire the value of the test data. According to the lithium battery health state prediction method provided by the invention, the accuracy and the precision of battery SOH value prediction are high, and the uncertainty is reduced.

Description

technical field [0001] The invention relates to the field of electrochemistry, in particular to a lithium battery health state prediction method based on a neural network kernel function GPR. Background technique [0002] At present, with the wide application of lithium-ion batteries, their reliability and safety in the process of storage, use and maintenance must be highly concerned. ) research has very important practical significance. [0003] Lithium-ion battery SOH is used to describe the state of health of the battery, which represents the life of the battery. Generally speaking, it is how long the battery can be used. The standard definition of SOH is the ratio of the capacity released by the power battery from the full state to the cut-off voltage at a certain rate under standard conditions and the corresponding nominal capacity, which is a response to the health of the battery. Simply put, it is the ratio of the actual value to the nominal value of some performanc...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/367
Inventor 付作娴宋显华袁丽丽王北一
Owner HARBIN UNIV OF SCI & TECH
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