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Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries

A technology of Gaussian process regression and health status, which is applied in the field of electrochemistry and analytical chemistry, and can solve problems such as poor adaptability

Inactive Publication Date: 2012-11-28
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a method for predicting the health status of lithium batteries based on Gaussian process regression for the poor adaptability of traditional lithium battery health prediction

Method used

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  • Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries
  • Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries
  • Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries

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specific Embodiment approach 1

[0030] Specific implementation mode one, the following combination figure 1 To describe this embodiment,

[0031] A lithium battery health prediction method based on Gaussian process regression, which is realized by the following steps:

[0032] Step 1. Discharge the new lithium battery to be tested, and then fully charge it, repeat charging and discharging N times, where N is an integer greater than or equal to 20, record the battery capacity of the lithium battery in the cycle, and then draw the lithium battery The relationship curve between the state of health SOH of the battery and the charge and discharge cycle, that is, the degradation curve with regeneration phenomenon;

[0033] Step 2, select the covariance function according to the degradation curve and the constraint condition with regeneration phenomenon; the constraint condition is that the covariance matrix formed by the selected covariance function satisfies the non-negative definite;

[0034] The covariance fu...

specific Embodiment approach 2

[0054] Specific implementation mode two, the following combination Figure 1 to Figure 5 Describe this embodiment. This embodiment is a further description of the state of health SOH of the battery in Embodiment 1. The specific expression of the state of health SOH of the battery in this embodiment is as follows:

[0055] SOH = C i C 0 × 100 %

[0056] where C i is the capacity value of the i-th charge-discharge cycle, C 0 is the initial capacity, i is a positive integer greater than or equal to 0.

specific Embodiment approach 3

[0057] Specific implementation mode three, the following combination Figure 1 to Figure 5 Describe this embodiment. This embodiment is a further description of the function selected in step 2 of embodiment 1 as a periodic function. The function selected in step 2 of this embodiment is a periodic function, a square exponential function, and a constant covariance A combination of functions as the covariance function; where the squared exponential covariance function is:

[0058] k f = σ y 2 exp ( - ( x - x ′ ) 2 2 l 2 )

[0059]...

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Abstract

The invention discloses a Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries, relates to a method for predicting the SOH of the lithium batteries, belongs to the fields of electrochemistry and analytic chemistry and aims at the problem that the traditional lithium batteries are bad in health condition prediction adaptability. The method provided by the invention is realized according to the following steps of: I. drawing a relation curve of the SOH of a lithium battery and a charge-discharge period; II, selecting a covariance function according to a degenerated curve with a regeneration phenomenon and a constraint condition; III, carrying out iteration according to a conjugate gradient method, then determining the optimal value of a hyper-parameter and bringing initial value thereof into prior distribution; IV, obtaining posterior distribution according to the prior part; V, obtaining the mean value and variance of predicted output f' without Gaussian white noise; and VI, together bringing the practically predicted SOH of the battery and the predicted SOH obtained in the step V into training data y to obtain the f', then determining the prediction confidence interval and predicting the SOH of the lithium battery. The method provided by the invention is used for detecting lithium batteries.

Description

technical field [0001] The invention relates to a method for predicting the health status of a lithium battery, belonging to the fields of electrochemistry and analytical chemistry. Background technique [0002] Lithium batteries are very promising energy sources in electronic devices due to their high energy rate and power. In electronic equipment, lithium-ion batteries are very important components and play a vital role in a specific system. Its failure will lead to system performance degradation, operational errors, and more serious catastrophic accidents. . [0003] Through our effective management of battery health, including setting battery operating conditions and planning battery replacement intervals, the reliability and stability of the entire system can be enhanced to a certain extent. However, since we rely on the integration of parameters in the process of managing and predicting the health of the battery, and the direct measurement value will be subject to no...

Claims

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Application Information

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IPC IPC(8): G01R31/36
Inventor 刘大同周建宝庞景月罗悦王红彭宇
Owner HARBIN INST OF TECH
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