Lithium ion battery remaining life prediction method based on fusion of gating circulation unit neural network and Kalman filtering model

A lithium-ion battery and Kalman filter technology, which is applied in biological neural network models, neural learning methods, and electrical measurement, can solve the problems of low adaptability to different working conditions and poor fitting ability of nonlinear degradation processes, etc. The effect of small noise

Active Publication Date: 2019-09-10
HARBIN INST OF TECH
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Problems solved by technology

[0003] The present invention aims to solve the problems of poor fitting ability of nonlinear degradation process and low adaptability to different working states in the existing remaining life prediction method of lithium-ion battery based on fusion model, and now provides a method based on Gated Recurrent Unit (Gated Recurrent Unit , GRU) neural network and Kalman filter (Kalman Filter, KF) model fusion lithium-ion battery remaining life prediction method

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  • Lithium ion battery remaining life prediction method based on fusion of gating circulation unit neural network and Kalman filtering model
  • Lithium ion battery remaining life prediction method based on fusion of gating circulation unit neural network and Kalman filtering model
  • Lithium ion battery remaining life prediction method based on fusion of gating circulation unit neural network and Kalman filtering model

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specific Embodiment approach 1

[0027] Specific implementation mode one: refer to figure 1 Specifically explain this embodiment, the method for predicting the remaining life of a lithium-ion battery based on the fusion of the gated recurrent unit neural network and the Kalman filter model described in this embodiment, takes an 18650 battery with a rated capacity of 2200mAh for testing, and the specific steps of the test are as follows:

[0028] Step 1: Construct a data set using the battery capacity data in each charge and discharge cycle of the lithium-ion battery for training, and use the data in the data set as training data.

[0029] Step 2, use the training data to construct the training set of the GRU model, the training set is expressed as [xtrain, ytrain], where:

[0030]

[0031] In the above formula, Cap l Indicates the capacity of the first battery, Indicates the number of charge and discharge cycles corresponding to when the capacity of the first battery degrades to 80% of the rated capacit...

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Abstract

The invention discloses a lithium ion battery remaining life prediction method based on fusion of a gating circulation unit neural network and a Kalman filtering model, and relates to the technical field of lithium ion battery health state detection. The objective of the invention is to solve the problems of poor fitting capability and low adaptability to different working states in a nonlinear degradation process of an existing lithium ion battery residual life prediction method based on a fusion model. According to the method, through establishing a GRU-RNN deep network model, the lithium ion battery capacity degradation characteristics are extracted by using the strong characteristic extraction capability of the GRU deep learning model on the time sequence, so that a more accurate battery capacity prediction model is obtained, and finally, noise is reduced and a more accurate prediction value is obtained through a KF filtering method.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion battery health state detection, and in particular relates to battery life prediction technology. Background technique [0002] At present, the methods for predicting the Remaining Useful Life (RUL) of lithium-ion batteries are roughly divided into two categories: physical model-based and data-driven model. Among them, the data-driven method does not need to clarify the degradation mechanism of the battery, so the related research is more in-depth. Data-driven methods include one type of statistical data-driven method based on statistical filtering and another type of data-driven method based on machine learning methods. The lithium-ion battery degradation model used in the data-driven method based on statistical filtering is relatively simple, and has poor adaptability to life prediction problems of different types of batteries and different usage conditions. The data-driven method based on ...

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Application Information

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IPC IPC(8): G06F17/50G06N3/08G01R31/392G01R31/367
CPCG06N3/084G01R31/392G01R31/367G06F30/20
Inventor 刘大同彭喜元李律宋宇晨
Owner HARBIN INST OF TECH
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