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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 ability to predict the nonlinear degradation trend of battery capacity, etc., and achieve improved adaptability and good state. The effect of reducing the relative error of tracking ability and capacity prediction

Active Publication Date: 2015-07-08
HARBIN INST OF TECH
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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 1: see figure 1 Describe this embodiment, the method for predicting the cycle life of a fusion lithium-ion battery based on the EKF method and the NSDP-AR model described in this embodiment includes 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 offline the real capacity degradation data of the lithium-ion battery of the same type as the lithium-ion battery to be tested;

[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 according to the empirical degradation model of the lithium-ion battery to be tested and the NSDP-AR model, use the preprocessed data and use the EKF method. Determine the parameters of the state transition equation in the state space model of the lithium ion batter...

specific Embodiment approach 2

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

[0015] The singular points in the data are eliminated, and the trend smoothing is performed for the capacity regeneration phenomenon whose amplitude is too large.

[0016] The singular point contains data with large measurement error and erroneous data, and the capacity regeneration phenomenon with excessive amplitude is shown in the curve as several capacity rising parts in the overall downward trend, that is, the burr 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 method for predicting the cycle life of a fusion lithium-ion battery based on the EKF method and the NSDP-AR model described in the specific embodiment 1 is that:

[0018] In the second step, the method for constructing the 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: Based on the empirical degradation model of the lithium-ion battery to be tested Construct the state-space model for parameter estimation of the empirical degradation model of the lithium-ion battery to be tested: the Δt k is 1;

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

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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 the 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 physical model-based (Model-based Prognostics) and data-driven (Data-Driven) methods. For lithium batteries to be tested, most studies focus on data-driven methods. Data-driven methods include a class of statistical data-driven methods based on statistical filtering, such as Particle Filter (PF), Kalman Filter (KF) and Extended Kalman Filter (EKF). The state transition equation of the lithium battery can be predicted and updated, fully considering the internal state transition characteristics of the lithium battery to be tested, but a degradation model lacks good adaptability to different types of batteries and different working states; the other is based on pure data-driven methods For example, the Autoregressive...

Claims

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Application Information

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Patent Type & Authority Patents(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
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