Lithium ion battery maximum capacity recession curve reconstruction method based on neural network and migration model

Abstract

The invention discloses a neural network and migration model-based lithium ion battery electricity storage capacity recession curve reconstruction method, which comprises the following steps of S1, based on an existing accelerated aging data set, performing data processing to obtain a relation curve of incremental capacity and voltage change of a lithium ion battery in a single charging process; s2, determining input and output variables of a neural network according to a relationship curve of incremental capacity and voltage change in a single charging process, substituting accelerated aging test data into the neural network, and constructing a basic model for reconstructing a maximum capacity recession curve of the lithium ion battery; s3, according to the basic model and the charging and discharging data under the normal industrial use condition, selecting a reference capacity point, and establishing a migration model; and S4, using the migrated model to reconstruct a maximum capacity recession curve when the lithium ion battery is normally aged, and performing error analysis. The method provided by the invention has the advantages of less data demand, high precision and small error.

Description

technical field [0001] The invention relates to the field of lithium ion battery power supply, in particular to a method for reconstructing the decay curve of lithium ion battery storage capacity based on a neural network and a migration model. Background technique [0002] Lithium batteries have been widely used in vehicles, power sources, secondary charging and energy storage devices due to their high energy density, long service life, low self-discharge rate, and cleanness and reliability. The existing research results show that. Lithium batteries account for the largest share of electrochemical energy storage, reaching 86%. In view of the current requirements for energy conservation and emission reduction and the continuous development of integrated energy systems, lithium batteries play an increasingly important role in these systems, and their performance and service life are increasingly becoming our concerns. [0003] At present, the evaluation of the state of char...

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Problems solved by technology

However, electric vehicles may have incomplete charging and discharging problems during the charging and discharging process, and the generated data sets are not complete. If you want to use industrial data sets to replace laboratory data, these problems need to be solved urgently

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