A deep learning-based method for predicting the remaining life of fuel cells

A fuel cell and life prediction technology, which is applied in neural learning methods, measuring electronics, measuring devices, etc., can solve problems depending on battery life attenuation, achieve the effect of improving prediction accuracy and reducing computational complexity

Active Publication Date: 2022-06-03
山东凯格瑞森能源科技有限公司
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  • Claims
  • Application Information

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Problems solved by technology

However, the current fusion algorithm largely depends on whether the exponential or logarithmic model it constructs can accurately describe the attenuation of battery life.

Method used

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  • A deep learning-based method for predicting the remaining life of fuel cells
  • A deep learning-based method for predicting the remaining life of fuel cells
  • A deep learning-based method for predicting the remaining life of fuel cells

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Embodiment Construction

[0037] In order to make the above objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention will be described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, ...

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Abstract

The invention relates to a method for predicting the remaining service life of a fuel cell based on deep learning. The method adopts a deep neural network model to realize accurate prediction of fuel cell RUL, and belongs to the technical field of proton exchange membrane fuel cell health status monitoring. The specific steps are: S1: Obtain the monitoring data of the fuel cell, and perform noise reduction processing; S2: Fit the training data after noise reduction processing, and establish the nonlinear mapping relationship between the current input data and the target data; S3: Establish Loss function and optimize the parameters of the neural network model through the method of feedback derivation to obtain the optimal neural network model; S4: The input data of the prediction starting point is used as the input of the optimal DNN to realize iterative rolling prediction. S4 can be simply summarized as: iteratively use the current prediction output of the neural network as the input of the next prediction, and iteratively predict the voltage or power output, so as to realize the fuel cell RUL prediction.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a fuel cell based on deep learning, and belongs to the technical field of proton exchange membrane fuel cells. Background technique [0002] As an environmentally friendly and clean energy, fuel cells have a good prospect in the application of new energy fuel cell electric vehicles by virtue of their great advantages in power conversion efficiency. However, compared with other energy devices such as lithium-ion batteries, high operating cost, short life cycle and safe and reliable operation are still the main reasons for the widespread application of fuel cells. Such as the most popular proton exchange membrane fuel cell system (PEMFC), its main characteristics are fast start-up performance, high power density (3.8 ~ 6.5kW / m3) and low operating temperature (50 ~ 80 ℃). System Prediction and Health Management (PHM) is an emerging field of scientific and technological development, wh...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367G01R31/392G06N3/08G06N3/04
CPCG01R31/367G01R31/392G06N3/08G06N3/048G06N3/044G06N3/045Y02E60/50
Inventor 杨鑫冷承霖刘凯
Owner 山东凯格瑞森能源科技有限公司
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