Lithium ion battery remaining useful life prediction method based on fusion algorithm

Abstract

The invention relates to a lithium ion battery remaining useful life (RUL) prediction method based on a fusion algorithm and belongs to the technical field of battery management. The method comprisesthe following steps: S1, acquiring battery capacity attenuation data, and determining parameters of a RUL prediction model based on an optimal control algorithm; S2, fitting data of a training set, iteratively outputting an optimal control algorithm mode parameter filtering estimation value and a battery capacity attenuation data filtering estimation value, and obtaining an initial RUL predicted value by virtue of the model parameter filtering estimated value; S3, based on a difference value between the filtering estimation value of the optimal control algorithm and experimental data, buildingan original error sequence, taking the original error sequence as an input of a neutral network algorithm, performing continuous iteration training on the error sequence, and outputting a predictionresult of the error sequence; and S4, after the data of the training set is used, obtaining a final lithium ion battery RUL prediction result by synthesizing an initial predicted value of the optimalcontrol algorithm and the error sequence prediction result obtained by virtue of the neutral network algorithm.

Description

technical field [0001] The invention belongs to the technical field of battery management and relates to a method for predicting the remaining life of a lithium-ion battery based on a fusion algorithm. Background technique [0002] In recent years, due to its wide application in new energy vehicles and other technological fields, lithium-ion batteries have gradually become a research hotspot in the field of new energy vehicle power batteries due to their high energy density and long cycle life. However, as the battery capacity gradually decays with the increase of the number of cycles, the performance of the battery will deteriorate, and even cause economic losses and personal injuries. Therefore, battery health status monitoring based on lithium battery capacity decay has become an urgent problem to be solved. The remaining battery life can be defined as the number of charge and discharge cycles before the attenuation performance index of the battery reaches the specified ...

AI Technical Summary

Problems solved by technology

In the first category, the use of data-driven methods to infer or compensate physical models, the use of data-driven methods to estimate predictive measurements of model-based methods, the use of data-driven methods to estimate or adjust the parameters of physical methods, the use of filtering methods These four methods estimate / adjust the parameters of the data-driven method; but none of them mentions the use of data-driven algorithms to compensate the filtering model for error compensation to achieve lithium battery RUL prediction

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