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Lithium battery SOH (State of Health) prediction method based on neural network and periodic kernel functions GPR

A neural network and prediction method technology, which is applied in the field of lithium battery health state prediction based on neural network and periodic kernel function GPR, can solve the problems of low prediction accuracy and inaccurate prediction results, and achieve high prediction accuracy and prediction uncertainty. Low, the effect of improving the accuracy of the forecast

Active Publication Date: 2017-01-18
SHENZHEN ACAD OF METROLOGY & QUALITY INSPECTION
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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.

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  • Lithium battery SOH (State of Health) prediction method based on neural network and periodic kernel functions GPR
  • Lithium battery SOH (State of Health) prediction method based on neural network and periodic kernel functions GPR
  • Lithium battery SOH (State of Health) prediction method based on neural network and periodic kernel functions GPR

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[0037] 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.

[0038] 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 lithium battery SOH prediction method based on neural network and periodic kernel functions GPR. The method comprises that a covariance function is determined on the basis of the neural network kernel function and the periodic kernel function, and a GPR prediction model is constructed; a mean value function in the GPR prediction model and a hyper-parameter in the covariance function are initialized; a logarithm maximum likelihood estimation function is used to optimize a hyper-parameter; and training data and test data are input to the GPR prediction model to obtain a value of the test data. Via the lithium battery SOH prediction method, the SOH value of a battery can be predicted accurately and precisely, and the uncertainty is lower.

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 periodic 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): G06N3/08G06F17/15
CPCG06F17/15G06N3/08
Inventor 卢文斌周頔陈锐衡李名兆熊凯
Owner SHENZHEN ACAD OF METROLOGY & QUALITY INSPECTION
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