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EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting cycling life of fusion-type lithium ion battery

A lithium-ion battery and model fusion technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of low adaptability, poor prediction ability of battery capacity nonlinear degradation trend, etc., and achieve improved adaptability and good state The effect of tracking ability and capacity prediction relative error reduction

Active Publication Date: 2013-10-09
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem of low adaptability to different batteries and different working conditions in the current model-based methods, and to further solve the problem of poor prediction ability of the nonlinear degradation trend of battery capacity. The present invention provides a method based on EKF and NSDP- AR model fusion lithium-ion battery cycle life prediction method

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  • EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting cycling life of fusion-type lithium ion battery
  • EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting cycling life of fusion-type lithium ion battery
  • EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting cycling life of fusion-type lithium ion battery

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specific Embodiment approach 1

[0010] Specific implementation mode one: see figure 1 Describe the present embodiment, the EKF method and NSDP-AR model fusion type lithium ion battery cycle life prediction method based on the present embodiment described, it comprises the following steps:

[0011] Step 1: measure the capacity data of the lithium-ion battery to be tested online, save the data and preprocess the data, and measure the real capacity degradation data of the lithium-ion battery of the same type as the lithium-ion battery to be tested offline;

[0012] Step 2: Determine the parameters of the empirical degradation model of the lithium-ion battery to be tested based on the EKF method, construct a state-space model of the lithium-ion battery based on the empirical degradation model of the lithium-ion battery to be tested and the NSDP-AR model, and use the preprocessed data and according to the EKF method Determine the parameters of the state transition equation in the state space model of the lithium ...

specific Embodiment approach 2

[0014] Specific implementation mode two: see figure 1 This embodiment is described. The difference between this embodiment and the specific embodiment one based on the EKF method and NSDP-AR model fusion type lithium ion battery cycle life prediction method is that the method for preprocessing the data in the first step is as follows: :

[0015] Eliminate the singular points in the data, and smooth the trend of the capacity regeneration phenomenon with too large amplitude.

[0016] The singular points include data with large measurement errors and erroneous data, and the phenomenon of capacity regeneration with excessive magnitude is shown in the curve as several capacity rising parts in the overall downward trend, that is, the glitch part in the falling curve.

specific Embodiment approach 3

[0017] Specific implementation mode three: see figure 1 This embodiment is described. The difference between this embodiment and the EKF method and NSDP-AR model fusion lithium-ion battery cycle life prediction method described in the first embodiment is that:

[0018] In said step 2, the method for constructing a lithium-ion battery state-space model according to the empirical degradation model of the lithium-ion battery to be tested and the NSDP-AR model includes the following steps:

[0019] Step A: According to the empirical degradation model of the lithium-ion battery to be tested Construct a state-space model for parameter estimation of the empirical degradation model of the Li-ion battery under test: the Δt k is 1;

[0020] a k = a k - 1 ...

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Abstract

The invention relates to an EKF (extended Kalmar filter)-method and NSDP-AR-model-based method for predicting the cycling life of a fusion-type lithium ion battery, relates to a method for predicting the cycling life of the lithium ion battery, and aims at solving the problems of the existing model-based method that the applicability to different batteries and different working states is low and the battery capacity nonlinear degradation tendency prediction capacity is poor. The method comprises the following steps of: online preprocessing measured capacity data of a lithium battery to be measured, and offline measuring real capacity degradation data of a lithium battery with the identical model number with the lithium battery to be measured; secondly, determining parameters of a lithium battery state space model based on an EKF method; carrying out the state estimation on the lithium battery to be measured according to an established lithium battery state space model, utilizing the output of the NSDP-AR model to update the state of the lithium battery to be measured, acquiring the battery capacity data of each charging-discharging cycle through the lithium battery state space model, comparing the data with the invalid threshold value of the lithium battery to be measured, and acquiring the residual service life of the lithium battery. The method is mainly applied to the field of the prediction for the cycling life of the battery.

Description

technical field [0001] The invention relates to a method for predicting cycle life of a lithium ion battery. 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 internal state transition characteristics of the lithium battery to be tested, but a certain degradation model lacks good adaptability to different type...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
Inventor 刘大同李君宝郭力萌彭宇彭喜元
Owner HARBIN INST OF TECH