A method for predicting the remaining life of lithium-ion batteries

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of long prediction period and low prediction accuracy

Active Publication Date: 2021-09-10
JIANGSU UNIV
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Problems solved by technology

However, the particle filter has a serious defect in the prediction function, that is, when there is no measured value in the prediction stage, the state of the particle filter cannot be updated, that is to say, the state value stays at the last known point, and the state value of the last known point is used. Point estimation is used to predict the remaining life of the battery, resulting in the longer the prediction period, the lower the prediction accuracy

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  • A method for predicting the remaining life of lithium-ion batteries
  • A method for predicting the remaining life of lithium-ion batteries
  • A method for predicting the remaining life of lithium-ion batteries

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Embodiment Construction

[0041] The technical solutions of the present invention will be further described in more detail below in conjunction with specific embodiments.

[0042] The battery data used in this embodiment is the data of the power battery laboratory of the Automotive Engineering Research Institute of Jiangsu University, using a 18650 type battery with a rated capacity of 2600mAh, a charge cut-off voltage of 4.2V, a discharge cut-off voltage of 2.75V, and 4 batteries.

[0043] The present invention provides a lithium-ion battery remaining life prediction method based on exponential smoothing prediction and particle filter fusion (ES-PF), the realization process is as follows:

[0044] Step 1: Carry out life cycle experiments on lithium-ion batteries, and process the data to obtain the change curve of the capacity of lithium-ion batteries with the number of cycles under different variables; wherein, the different variables include at least temperature, discharge cut-off voltage, charge cut-...

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Abstract

The invention discloses a method for predicting the remaining life of a lithium-ion battery. The state parameter change data of the battery model is obtained by using a particle filter algorithm, and the data is imported into an exponential smoothing prediction model (ES) to obtain a state parameter prediction value, which is then brought into the observation equation to obtain The observed and predicted value of the capacity, and finally the observed and predicted value is fed back to the particle filter to predict the remaining battery life (RUL). The ES-PF prediction model of the present invention can solve the problem that the state parameters of the particle filter algorithm cannot be updated in the prediction stage, causing the prediction error to become larger as the prediction period becomes longer, and effectively improve the prediction accuracy of the particle filter algorithm.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion batteries, and more specifically relates to a method for predicting the remaining life of lithium-ion batteries. Background technique [0002] Due to the advantages of high energy density, low self-discharge rate, no memory effect, and long cycle life, lithium-ion batteries are widely used in consumer electronics, electric vehicles and even aerospace as one of the most promising power sources. With the continuous charge and discharge cycle, the performance degradation of the battery, that is, aging, is inevitable. The deterioration of batteries can lead to performance degradation, financial loss, and even accidents. Therefore, it is very necessary to find an accurate and reliable battery remaining life prediction method to monitor battery decay and evaluate its reliability. [0003] The remaining battery life (RUL) prediction is to predict how many cycles / times will elapse from the current c...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/392G01R31/367
Inventor 盘朝奉陈瑶王丽梅何志刚陈伟鹤薛安荣蔡涛
Owner JIANGSU UNIV
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