Indirect health factor selecting method for lithium battery capacity estimating

Abstract

The embodiment of the invention provides an indirect health factor selecting method for lithium battery capacity estimating. The method comprises the steps of calculating the relevancy between the battery capacity and a plurality of backup indirect health factors; selecting N backup indirect health factors with the maximum relevancy from the multiple backup indirect health factors as initial indirect health factors; adopting the battery capacity corresponding to the initial indirect health factors and the initial indirect health factors as input of a related vector machine model, and performing model training on the related vector machine model to obtain a target training model; judging whether the target training model meets the preset precision requirement or not; if the target trainingmodel meets the preset precision requirement, adopting the initial indirect health factors as target indirect health factors so as to be used for estimating the lithium battery capacity; if the targettraining model does not meet the preset precision requirement, updating N as N+1. The purpose of indirect health factor selection can be well achieved.

Description

technical field [0001] The invention relates to the technical field of product life estimation, in particular to an indirect health factor selection method for lithium battery capacity estimation. Background technique [0002] Product life estimation refers to the estimation of the time for a certain product equipment from now to when it cannot be used at all or from now to when it cannot complete its specified functions. By estimating the life of the product, the product can be repaired and maintained in a timely manner, and the service life of the product can be improved or the occurrence of major safety accidents can be avoided. Lithium battery capacity estimation is essentially a problem of estimating the remaining life of the lithium battery product, and the selection of indirect health factors for lithium battery capacity estimation is actually a feature selection problem. At present, there have been some studies on the remaining battery life estimation at home and ab...

AI Technical Summary

Problems solved by technology

[0018] In order to solve the above technical problems, an embodiment of the present invention provides an indirect health factor selection method for lithium battery capacity estimation to solve the problem of how to screen out appropriate indirect health factors for lithium ion battery capacity estimation in the prior art to meet the accuracy requirements of Li-ion battery capacity estimation

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