Lithium battery state of health prediction method based on neural network and Maternard kernel function GPR

A neural network and prediction method technology, which is applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of low prediction accuracy and inaccurate prediction results, and achieve high prediction accuracy, low prediction uncertainty, and improved prediction. The effect of accuracy

Inactive Publication Date: 2017-02-15
HARBIN UNIV OF SCI & TECH
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[0017] However, the current state-of-health prediction methods for lithium b

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  • Lithium battery state of health prediction method based on neural network and Maternard kernel function GPR
  • Lithium battery state of health prediction method based on neural network and Maternard kernel function GPR
  • Lithium battery state of health prediction method based on neural network and Maternard kernel function GPR

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[0039] 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.

[0040]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 s...

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Abstract

The invention provides a lithium battery state of health prediction method based on a neural network and Maternard kernel function GPR. The method comprises the steps that a covariance function is determined based on a neural network kernel function and a Maternard kernel function so as to construct a GPR prediction model; the mean value function in the GPR prediction model and the hyper-parameter in the covariance function are initialized; the hyper-parameter is optimized by using a logarithmic maximum likelihood estimation function; and training data and test data are inputted to the GPR prediction model so as to acquire the value of the test data. According to the lithium battery state of health prediction method, the prediction accuracy and precision of the battery SOH value are enabled to be high, and the uncertainty is low.

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 and a Maternard 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...

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Application Information

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IPC IPC(8): G01R31/36
CPCG01R31/392G01R31/367
Inventor 袁丽丽宋显华付作娴王北一
Owner HARBIN UNIV OF SCI & TECH
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