Lithium ion battery residual life prediction method based on time convolutional neural network

Abstract

The invention discloses a lithium ion battery residual life prediction method based on a time convolutional neural network, and belongs to the field of lithium ion battery fault prediction and health management. The battery equipment degradation process is a highly nonlinear and complex multi-dimensional system and is high in time-varying property, the prediction process of an existing algorithm needs expert knowledge and priori knowledge, time and labor are wasted, and the prediction process is difficult and low in precision. According to the method, a hidden model in a time sequence is mined through the powerful time sequence modeling capability of the neural network, and the nonlinear mapping relation between measured parameters and the service life is automatically established. Due to a parallelism mechanism of convolution calculation, accelerated training can be carried out by using graphic calculation, and the calculation is faster. The invention provides the calculation method of a parameter filter, when invalid parameters and redundant parameters are too many, a part of parameters can be automatically screened, the prediction workload is reduced, and the training efficiency is improved.

Description

technical field [0001] The remaining life prediction method of lithium-ion batteries based on temporal convolutional neural network belongs to the field of fault prediction and health management of lithium-ion batteries. Background technique [0002] Lithium batteries have the advantages of high output voltage, high energy density, low self-discharge rate, long cycle life and high reliability. important fields such as aerospace. If a battery failure occurs during use, it is likely to cause performance degradation or failure of the corresponding power equipment or system, thereby increasing costs, and even leading to accidents such as fire and explosion. Therefore, it is of great practical significance to accurately predict the remaining life of the battery. [0003] There are three typical lithium-ion battery remaining useful life (RUL) prediction methods: the first is an experience-based method, the second is a model-based method, and the third is a data-driven method. T...

AI Technical Summary

Problems solved by technology

However, the recurrent neural network and its variants cannot be calculated in parallel at runtime, and the graphics computing power of the GPU cannot be used to accelerate its training, which affects computing efficiency.

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