Check patentability & draft patents in minutes with Patsnap Eureka AI!

Method for predicting cycle life of lithium ion battery based on NSDP-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 poor prediction ability of battery capacity nonlinear degradation characteristics, and achieve the effect of improving prediction ability and optimizing prediction effect

Active Publication Date: 2013-11-20
HARBIN INST OF TECH
View PDF5 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention is to solve the problem that the AR model has poor predictive ability on the nonlinear degradation characteristics of the battery capacity, and proposes a lithium-ion battery cycle life prediction method based on the NSDP-AR model

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for predicting cycle life of lithium ion battery based on NSDP-AR (AutoRegressive) model
  • Method for predicting cycle life of lithium ion battery based on NSDP-AR (AutoRegressive) model
  • Method for predicting cycle life of lithium ion battery based on NSDP-AR (AutoRegressive) model

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0025] Specific implementation mode one: see figure 1 Describe this embodiment, the lithium-ion battery cycle life prediction method based on the NSDP-AR model described in this embodiment is:

[0026] Step 1: According to the AR model of the lithium-ion battery to be predicted Predict the capacity of the lithium-ion battery to be predicted, and obtain the capacity prediction sequence ARpredict;

[0027] in is the autoregressive coefficient, p is the optimal model order, a t ,t=0,±1,… are mutually independent white noise sequences with a mean of 0 and a variance of normal distribution of

[0028] Step 2: According to the capacity prediction sequence ARpredict obtained in step 1, extract the approximate life cycle percentage kp' sequence;

[0029] Step 3: Before the lithium-ion battery to be predicted is put into online use, carry out the charging and discharging test of the offline test platform on the simulated online condition for each battery in the fitting group, a...

specific Embodiment approach 2

[0043] Specific implementation mode two: see figure 2 Describe this embodiment, this embodiment is a further limitation to the specific embodiment one, the AR model of the lithium-ion battery to be predicted in the first step The build method is:

[0044] Step 11: Obtain the optimal model order p of the AR model;

[0045] Step 1 and 2: Autoregressive coefficient of AR model fusion of

[0046] Select the Yule-Wallker method and the Burg method to independently obtain the model coefficients, and then perform dynamic linear combination to output the final coefficient results;

[0047] Step 13: According to the optimal model order p obtained in step 11 and the autoregressive coefficient obtained in step 12 Build an AR model of the lithium-ion battery to be predicted.

specific Embodiment approach 3

[0048] Specific implementation mode three: see image 3 Describe this embodiment, this embodiment is the further limitation to specific embodiment two, the method for obtaining the optimal model order p of AR model in described step one by one is:

[0049] Step A: Extract the historical capacity data F of the lithium-ion battery to be predicted as the original data input for order judgment;

[0050] Step B: Standardize historical capacity data F to obtain standardized data Y:

[0051] Step C: judging whether the standardized data Y is suitable for AR modeling;

[0052] Step D: Judge the model order of the AR model according to the AIC criterion, and obtain the optimal order p.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a method for predicting the cycle life of a lithium ion battery based on an NSDP-AR (AutoRegressive) model, namely the method for predicting the cycle life of the lithium ion battery, which solves the problem that an AR model has a poorer predicating capacity to the nonlinear degradation characteristics of battery capability. The method comprises the following steps: according to the AR model of the lithium ion battery to be predicted, predicting the capability of the lithium ion battery; according to a capability predicting sequence ARpredict, extracting the sequence of approximate whole life cycle percentage kp; before the lithium ion battery to be predicted is used on line, conducting a charging and discharging test to each battery of a fitting group, establishing the NSDP-AR model of each battery of the fitting group, and relatively analyzing the capability degradation trends of the lithium ion battery to be predicted and each battery of the fitting group to obtain a relevancy degree ri; adopting a weighted method based on the relevancy degree to determine the parameter estimation result of nonlinear degradation factors KT of the lithium ion battery to be predicted on line, and nonlinearly correcting the capability predicting result ARpredict. The method is suitable for predicting the cycle life of the lithium ion battery.

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 using a nonlinear AR model. 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 ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
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
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com