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Self-adaptive GRNN estimation method for health state of lithium ion battery of electric vehicle

A lithium-ion battery, electric vehicle technology, applied in neural learning methods, measuring electricity, measuring electrical variables, etc., can solve problems such as overfitting

Active Publication Date: 2020-05-05
JIANGSU UNIV
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  • Description
  • Claims
  • Application Information

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Problems solved by technology

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

Method used

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  • Self-adaptive GRNN estimation method for health state of lithium ion battery of electric vehicle
  • Self-adaptive GRNN estimation method for health state of lithium ion battery of electric vehicle
  • Self-adaptive GRNN estimation method for health state of lithium ion battery of electric vehicle

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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 invention provides a self-adaptive GRNN estimation method for the health state of a lithium ion battery of an electric vehicle. Aiming at the characteristics of missing, abnormality and noise of battery measurement data, an improved particle filter algorithm is adopted for processing or a least square method and a mean value replacement method are selected for processing parameters according to a variable coefficient so that the input parameters of the neural network are stable, and the noise immunity is improved. The GRNN algorithm has the advantage of high estimation precision when applied to SOH estimation, but due to the fact that a smoothing factor is manually set, the experiment average error and variance of the smoothing factor are not stable. Therefore, the smoothing factor ofthe GRNN is optimized by using the QGA so as to improve the network adaptability. Furthermore, in consideration of the characteristic that the correlation between different characteristic parameters and the capacity is different, a transfer function of a mode layer is constructed by utilizing the optimal smoothing factor and the correlation coefficient so as to improve the estimation precision ofthe GRNN. Experimental results show that the algorithm provided by the invention can effectively estimate the health state of the lithium ion battery and has a wide application prospect.

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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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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