Lithium battery life prediction method based on LSTM network and transfer learning

Abstract

The invention provides a lithium battery residual life prediction method based on an LSTM neural network and transfer learning. The lithium battery residual life prediction method comprises the following steps: step 1, carrying out data acquisition and data preprocessing; 2, dividing the data into a training set and a test set in proportion; 3, building a source domain LSTM neural network model, inputting the source domain data training set into a neural network for training, and inputting the data of the test set into the neural network for testing; 4, measuring the data difference between the source domain and the target domain by using the maximum mean value difference to obtain the distribution distance between the source domain and the target domain; and step 5, adjusting the source domain network model according to the maximum mean value difference to obtain a target domain network model, migrating source domain network model parameters, and inputting target domain data into themodel to perform residual life prediction. By migrating the structure and parameters of the network model, the network training time is reduced, and the efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion batteries, and in particular relates to a battery life prediction method based on LSTM network and transfer learning. Background technique [0002] Lithium-ion batteries are widely used in various electronic devices, automotive energy and aerospace because of their long life, fast charging, high energy, small size, and no pollution. In the actual application process, the capacity of lithium-ion batteries will decrease with the increase of the number of charge and discharge cycles, and the performance will gradually degrade. The problem of battery life failure may lead to safety accidents, so battery life prediction is particularly important. Lithium-ion battery remaining life research can be summarized into two categories: model-based prediction and data-driven prediction. There are many data-driven RUL prediction research methods, including: artificial neural network, support vector machine,...

AI Technical Summary

Problems solved by technology

[0003] LSTM is a variant of the cyclic neural network RNN. RNN is suitable for processing time series, but its reverse error will be transmitted as the number of layers increases during the training process. The error value is getting smaller and smaller, and the gradient disappears and the gradient explodes. , only suitable for processing short time series

Images