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An Adaptive GRNN Method for Estimating the State of Health of Lithium-ion Batteries in Electric Vehicles

A lithium-ion battery and electric vehicle technology, applied in neural learning methods, measuring electricity, measuring electrical variables, etc., can solve problems such as overfitting, achieve improved noise resistance, broad application prospects, and improve network adaptability Effect

Active Publication Date: 2021-11-23
JIANGSU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If the value of the smoothing factor is too small, overfitting will occur

Method used

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  • An Adaptive GRNN Method for Estimating the State of Health of Lithium-ion Batteries in Electric Vehicles
  • An Adaptive GRNN Method for Estimating the State of Health of Lithium-ion Batteries in Electric Vehicles
  • An Adaptive GRNN Method for Estimating the State of Health of Lithium-ion Batteries in Electric Vehicles

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Embodiment Construction

[0054] Taking the data subset of the No. 5 battery in the NASA public data set as an example, the technical solutions in the embodiments of the present invention are clearly and completely described in combination with the drawings in the embodiments of the present invention.

[0055] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0056] Such as figure 1 As shown, according to an embodiment of the present invention, the method for estimating the state of health of an electric vehicle lithium-ion battery using adaptive GRNN includes four basic steps:

[0057] 1. Process the battery data bas...

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Abstract

The present invention proposes an adaptive GRNN estimation method for the state of health of lithium-ion batteries of electric vehicles. In view of the characteristics of lack, abnormality and noise in the battery measurement data, according to the coefficient of variation, the improved particle filter algorithm is used to process or the least square method and mean value replacement method are selected to process parameters to make the input parameters of the neural network stable, thereby improving noise resistance. The application of GRNN algorithm to SOH estimation has the advantage of high estimation accuracy, but the experimental average error and variance are unstable due to the artificial setting of the smoothing factor. Therefore, the present invention utilizes QGA to optimize the smoothing factor of GRNN to improve network adaptability. Further, considering that there are differences in the correlation between different feature parameters and capacity, the present invention uses the optimal smoothing factor and correlation coefficient to construct the transfer function of the mode layer to improve the estimation accuracy of the GRNN. Experimental results show that the algorithm proposed by the invention can effectively estimate the state of health of lithium-ion batteries and has broad application prospects.

Description

technical field [0001] The invention belongs to the technical field of electric vehicle batteries, and relates to a method for estimating the state of health of a lithium-ion battery, in particular to a method for estimating the state of health of an adaptive GRNN for an electric vehicle lithium-ion battery. Background technique [0002] With the global implementation of energy and environmental protection strategies, lithium-ion electric vehicles have developed rapidly due to their high energy efficiency and environmental friendliness. The health status of lithium-ion batteries is an important indicator of the safety and reliability of electric vehicles, so it is of great significance to realize the effective estimation of the health status of lithium-ion batteries. The battery state of health SOH is difficult to measure directly, and it is mainly estimated through directly measurable battery characteristics such as charge and discharge current and voltage. [0003] Since ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/392G01R31/367G06N3/06G06N3/04G06N3/08
CPCG01R31/392G01R31/367G06N3/061G06N3/08G06N3/045
Inventor 薛安荣杨婉琳于彬鹏陈伟鹤蔡涛盘朝奉何志刚李骁淳王丽梅
Owner JIANGSU UNIV
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