Lithium battery residual life prediction method

Abstract

The invention discloses a lithium battery residual life prediction method comprising the following steps: 1, offline modeling, collecting lithium battery offline data, extracting a health factor sample set, using a random forest algorithm to carry out weight analysis on the health factor sample set, determining a selected health factor sample, and carrying out BiLSTM network model training to obtain a health factor model; optimal hyper-parameters of the model are selected through Bayesian optimization, and a prediction model is constructed; 2, on-line prediction: obtaining a health factor sample set through lithium battery on-line data and feature selection corresponding to an off-line stage; and predicting the service life of the lithium battery by using the prediction model in the step 1. According to the invention, while the prediction accuracy of the neural network is maintained, the number of parameters is reduced, the complexity of parameter training is reduced, the loss caused by failure of the lithium battery is reduced, the safety of the lithium battery is improved, and the problems of redundancy and insufficiency in selection of health factors of the lithium battery and selection complexity of different hyper-parameters of the neural network are solved.

Description

technical field [0001] The invention belongs to the technical field of lithium batteries, and in particular relates to a method for predicting the remaining life of a lithium battery. Background technique [0002] Since its inception, lithium batteries have been widely used in electronic products, aerospace, electric vehicles and other equipment because of their excellent electrochemical properties. However, in the process of use, the capacity of lithium-ion batteries will gradually decrease with the increase of the number of cycles, which brings safety and reliability problems. The accurate prediction of the remaining service life of lithium-ion batteries is of great significance for the safe operation of the power system. [0003] At present, the commonly used lithium battery life prediction methods are: traditional model method, electrochemical model method, and data-driven method. Because the internal chemical reactions of lithium batteries are very complex, the establ...

AI Technical Summary

Problems solved by technology

However, the current data-driven method has the following two problems: (1) The selection of health factors is redundant and insufficient, which affects the accuracy of the remaining life prediction of lithium batteries; (2) Different hyperparameter selection of the neural network will affect the accuracy of the model , usually requires a large number of hyperparameter experiments to determine the final model parameters, which is time-consuming and labor-intensive

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