Online prediction method of lithium battery capacity based on k-means clustering and elman neural network

Abstract

The present invention proposes a K On-line prediction method of lithium battery capacity based on mean value clustering and Elman neural network. First, determine the model of the lithium-ion battery to be tested, use a battery of the same type as the battery to be tested to conduct a cycle charge and discharge experiment, record the discharge time series of the lithium battery and make a K Mean value clustering and establishment of data model; then, the introduction of simulated annealing genetic algorithm to optimize the initial weight and threshold of the Elman neural network, using the built data model to train the Elman neural network, and establishing the actual capacity prediction system of lithium-ion batteries offline. When performing online capacity prediction, the collected actual discharge time series data of the lithium-ion battery to be tested is input into the prediction system, and the actual capacity of the battery can be predicted without affecting the normal operation of the lithium-ion battery. The technology of the invention can realize the online accurate prediction of the actual capacity of the lithium ion battery.

Description

technical field [0001] The invention belongs to the technical field of lithium batteries, in particular to an online lithium battery capacity prediction method based on K-means clustering and Elman neural network. Background technique [0002] As the main energy storage device of contemporary electronic products, lithium batteries have basically replaced traditional nickel-cadmium batteries and nickel-metal hydride batteries due to their advantages such as lighter weight, lower discharge rate and long service life. Lithium batteries are also widely used in other industrial fields such as manned spacecraft and unmanned aircraft. Lithium batteries have become an important component to promote the healthy development of the national economy and the progress of national science and technology, and have played an important role in promoting industrial technological progress, new energy applications and the improvement of the ecological environment. [0003] Inevitably, there are...

AI Technical Summary

Problems solved by technology

In the actual prediction process, the autoregressive sliding model needs to estimate the order and unknown parameters of the summed autoregressive sliding average. The algorithm implementation is relatively complicated, and it is generally difficult to be widely used in engineering fields with high constraints on software and hardware resources; support vector machines Although it has a better artificial intelligence self-learning function, its model solving process is relatively complicated, and it is not easy to be popularized and applied in engineering; the particle filter algorithm relies too much on the battery experience degradation model when predicting the actual capacity of lithium batteries, and its online application ability is poor. ; The traditional neural network algorithm itself has problems such as easy to fall into local minimum, slow convergence speed, etc.

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