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Lithium ion battery cycle life predicating method based on cycle life degeneration stage parameter ND-AR (neutral density-autoregressive) model and EKF (extended Kalman filter) method

A lithium-ion battery, cycle life technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of poor prediction accuracy and poor adaptability of battery remaining life

Active Publication Date: 2013-11-20
HARBIN INST OF TECH
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  • Application Information

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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the linear AR model directly predicts the remaining life of the battery that presents nonlinear degradation characteristics over time and the poor adaptability of the model-based method to different batteries and different working conditions. Lithium-ion battery cycle life prediction method based on ND-AR model of phase parameters and EKF method

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  • Lithium ion battery cycle life predicating method based on cycle life degeneration stage parameter ND-AR (neutral density-autoregressive) model and EKF (extended Kalman filter) method
  • Lithium ion battery cycle life predicating method based on cycle life degeneration stage parameter ND-AR (neutral density-autoregressive) model and EKF (extended Kalman filter) method
  • Lithium ion battery cycle life predicating method based on cycle life degeneration stage parameter ND-AR (neutral density-autoregressive) model and EKF (extended Kalman filter) method

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specific Embodiment approach 1

[0024] Specific embodiments 1. The lithium-ion battery cycle life prediction method based on the ND-AR model of the cycle life degradation stage parameters and the EKF method described in this embodiment, the specific steps of the method are:

[0025] Step 1. Measure the capacity data of the lithium battery to be tested online, save the data and preprocess the data;

[0026] Step 2. Determine the parameters of the online lithium-ion battery empirical degradation model based on the EKF method;

[0027] Construct the state transition equation in the lithium-ion battery state-space model according to the lithium-ion battery empirical degradation model, use the preprocessed data and determine the empirical degradation model of the lithium-ion battery according to the EKF method and the weighted parameter calculation method based on the prediction probability parameter;

[0028] Step 3, using the preprocessed data to determine the AR model of the online battery by using the fusion...

specific Embodiment approach 2

[0041] Specific embodiment 2. This embodiment is a further description of the lithium-ion battery cycle life prediction method based on the ND-AR model and the EKF method of the cycle life degradation stage parameters described in the specific embodiment 1. The online measurement described in step 1 For the capacity data of the lithium battery to be tested, the method of saving the data and preprocessing the data is to eliminate the singular points in the data, and to smooth the trend of the capacity regeneration phenomenon with an excessive amplitude.

specific Embodiment approach 3

[0042] Specific Embodiment Three. This embodiment is a further description of the lithium-ion battery cycle life prediction method based on the ND-AR model and the EKF method of the cycle life degradation stage parameters described in the specific embodiment one. The method of determining the AR model of the online battery using the fusion autoregressive coefficient calculation method for the processed data is as follows:

[0043] Step 21. Use the preprocessed data to obtain the AR model according to the AIC criterion

[0044]

[0045] The model order p of

[0046] Step 22: Use the preprocessed data to obtain the autoregressive coefficients of the AR model according to the Yule-Wallker method and the Burg method respectively, and output the final autoregressive coefficients using the dynamic linear fusion method for the two obtained autoregressive coefficients coefficient

[0047] Step 23, according to the model order p obtained in step E and the final autoregressive co...

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Abstract

The invention provides a lithium ion battery cycle life predicating method based on a cycle life degeneration stage parameter ND-AR (neutral density-autoregressive) model and an EKF (extended Kalman filter) method, and relates to the lithium ion battery cycle life predicating method. The method comprises the steps that the volume data of a lithium ion battery to be measured is measured in an on-line way, and the data is stored and is preprocessed; the parameters of an on-line lithium ion battery experience degradation model is determined on the basis of the EKF method; an AR (autoregressive) model of an on-line battery is determined through the preprocessed data by a fusion autoregressive coefficient solving method; a battery with the same model as the lithium ion battery to be measured is subjected to off-line state simulation on on-line condition charge and discharge testing, volume degradation models of the lithium ion battery to be predicated and the battery with the same model as the lithium ion battery to be measured are subjected to correlation analysis, the battery volume data in each charge and discharge circulation is compared with the failure threshold value of the lithium ion battery to be measured, a RUL (remaining useful life) is obtained, and the cycle life predication of the lithium ion battery is completed. The lithium ion battery cycle life predicating method is applied to battery life predication.

Description

technical field [0001] The invention relates to a lithium ion battery cycle life prediction method, in particular to a lithium ion battery cycle life prediction method based on the fusion of an ND-AR model based on cycle life stage parameters and an EKF method. Background technique [0002] At present, the methods for predicting the Remaining Useful Life (RUL) of lithium-ion batteries are roughly divided into Model-based Prognostics and Data-Driven methods. For electronics with complex failure mechanisms and difficult models to establish For lithium batteries to be tested, most of the research focuses on data-driven methods. The data-driven method includes a class of statistical data-driven methods based on statistical filtering, such as particle filter (Particle Filter, PF), Kalman filter (Kalman Filter, KF) and extended Kalman filter (Extended Kalman Filter, EKF). The state transition equation of the measured lithium battery is predicted and updated, fully considering the...

Claims

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Application Information

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IPC IPC(8): G01R31/36
Inventor 彭宇郭力萌刘大同马云彤彭喜元
Owner HARBIN INST OF TECH
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