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

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...

AI Technical Summary

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.

Images