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Method for predicting cycle life of fused lithium ion battery based on EKF (Extended Kalman Filter) method and AR (AutoRegressive) model

A lithium-ion battery and AR model technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problem of low adaptability and achieve the effects of improved adaptability, reduced relative error, and improved prediction effect

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

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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that these current model-based methods have low adaptability to different batteries and different working conditions. The present invention provides a lithium-ion battery cycle life prediction method based on EKF method and AR model fusion

Method used

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  • Method for predicting cycle life of fused lithium ion battery based on EKF (Extended Kalman Filter) method and AR (AutoRegressive) model
  • Method for predicting cycle life of fused lithium ion battery based on EKF (Extended Kalman Filter) method and AR (AutoRegressive) model
  • Method for predicting cycle life of fused lithium ion battery based on EKF (Extended Kalman Filter) method and AR (AutoRegressive) model

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specific Embodiment approach 1

[0014] Specific implementation mode one: combine figure 1 Describe this embodiment, the EKF method and AR model fusion type lithium-ion battery cycle life prediction method based on this embodiment described, it comprises the following steps:

[0015] Step 1: Measure the capacity data of the lithium battery to be tested online, save the data and preprocess the data;

[0016] Step 2: Determine the parameters of the lithium-ion battery state space model based on the EKF method:

[0017] Construct lithium-ion battery state-space model according to lithium-ion battery empirical degradation model and AR model, utilize the data after pretreatment and determine the parameter of described lithium-ion battery state-space model according to EKF method; The prediction output value of described AR model and observation The observed value sequence after the noise superimposition is the observed value of the battery capacity of the state space model of the lithium-ion battery, and the AR m...

specific Embodiment approach 2

[0019] Specific embodiment 2: This embodiment is a further limitation of the method for predicting the cycle life of a lithium-ion battery based on the EKF method and the AR model fusion described in the specific embodiment 1.

[0020] The method for preprocessing the data in the step 1 is:

[0021] Eliminate the singular points in the data, and smooth the trend of the capacity regeneration phenomenon with too large amplitude.

[0022] 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

[0023] Specific embodiment three: this embodiment is a further limitation of the cycle life prediction method for lithium-ion batteries based on the EKF method and AR model fusion described in specific embodiment one,

[0024] In step 2, the described lithium-ion battery state-space model is constructed according to the lithium-ion battery empirical degradation model and the AR model, and the method for determining the parameters of the lithium-ion battery state-space model according to the EKF method using the preprocessed data includes the following steps :

[0025] Step A: Empirical Degradation Model Based on Li-ion Batteries Construct a state-space model of the parameter estimates for the degradation model:

[0026] a k = a k - 1 + ...

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Abstract

The invention discloses a method for predicting the cycle life of a fused lithium ion battery based on an EKF (Extended Kalman Filter) method and an AR (AutoRegressive) model, namely the method for predicting the cycle life of the lithium ion battery. The purpose is to solve the problem that the current methods based on models have a low adaptive capacity to different batteries and different working states. The method comprises the following steps: 1, measuring the capability data of the lithium ion battery to be measured on line, storing the data, and preprocessing the data; 2, based on the EKF method, determining the parameters of the state space model of the lithium ion battery; 3, according to the established state space model of the lithium ion battery, estimating the state of the lithium ion battery to be measured, and utilizing the output of the AR model to update the state of the lithium ion battery to be measured; causing the state space model of the lithium ion battery to obtain the capability data of the battery in each charging and discharging cycle, and comparing the data with the failure threshold of the lithium ion battery to be measured to obtain the residual life of the lithium ion battery. The method is used for predicting the cycle life of the lithium ion battery.

Description

technical field [0001] The invention relates to a method for predicting the cycle life of a lithium-ion battery, in particular to a method for predicting the cycle life of a lithium-ion battery based on an EKF method and an AR model fusion. 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 trans...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
Inventor 刘大同马云彤郭力萌彭宇彭喜元
Owner HARBIN INST OF TECH
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