Lithium battery capacity online prediction method based on K-means clustering and Elman neural network

A neural network and prediction method technology, applied in the field of lithium battery capacity online prediction based on K-means clustering and Elman neural network, can solve the problems of poor online application ability, easy to fall into local minimum, difficult to popularize and apply, etc.

CN110687452AActive Publication Date: 2020-01-14NANJING UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2020-01-14

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Abstract

The invention provides a lithium battery capacity online prediction method based on K-means clustering and an Elman neural network. The method comprises the following steps: firstly, determining the model of a lithium ion battery to be tested, carrying out cyclic charging and discharging experiment by utilizing a battery with the same model as the battery to be tested, recording a lithium batterydischarging time sequence, carrying out K-means clustering on the lithium battery discharging time sequence, and establishing a data model; and then, introducing a simulated annealing genetic algorithm to optimize initial weight and threshold of the Elman neural network, training the Elman neural network by using the constructed data model, and establishing a lithium ion battery actual capacity prediction system offline. When capacity prediction is carried out online, the collected actual discharging time sequence data of the lithium ion battery to be tested is input into the prediction system, and the actual capacity of the battery is predicted while the normal work of the lithium ion battery is not influenced. According to the invention, online accurate prediction of the actual capacityof the lithium ion battery can be realized.
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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...

Examples

Embodiment 1

[0153] In order to illustrate the technical scheme and technical purpose of the present invention, the present invention will be further introduced below in conjunction with the accompanying drawings and specific embodiments.

[0154] combine figure 1 , a kind of lithium battery actual capacity prediction method based on K-means clustering and Elman neural network that the present invention proposes, comprises the following steps:

[0155] Step 1: Build a lithium battery actual capacity prediction data model through experiments

[0156] 1-1) Determine the model of the lithium battery to be tested, and use a brand new lithium battery of the same model as the battery to be tested to conduct a cycle charge and discharge experiment. The experimental process is: charge the lithium battery with a constant current of 1.5A until the battery terminal voltage reaches 4.2V, keep the battery terminal voltage at 4.2V, and continue charging in constant voltage mode until the charging curre...