Method for predicting residual life of proton exchange membrane fuel cell

Abstract

The invention discloses a method for predicting the residual life of a proton exchange membrane fuel cell. Firstly, obtaining output voltages of the proton exchange fuel cell at a plurality of continuous moments; constructing a training sample set, constructing a proton exchange membrane fuel cell residual life prediction model including one input layer, three hidden layers and one output layer; adopting an input layer and three hidden layers to construct a deep belief network model constructed by stacking three Gaussian restricted Boltzmann machines; and constructing an extreme learning machine model by adopting the last two hidden layers and the output layer, training the residual life prediction model by adopting the training sample set, acquiring the output voltage of the proton exchange membrane fuel cell at the latest moment, and predicting the residual service life of the fuel cell through the residual life prediction model. The method can effectively improve the accuracy and stability of the prediction result of the residual life of the proton exchange membrane fuel cell.

Description

technical field [0001] The invention belongs to the technical field of proton exchange membrane fuel cells, and more specifically relates to a method for predicting the remaining life of a proton exchange membrane fuel cell. Background technique [0002] Proton exchange membrane fuel cell (PEMFC) can directly convert the chemical energy stored in the fuel into electrical energy without greenhouse gas emissions, and is considered to be a promising power generation device to solve environmental crisis and energy problems. In addition, due to the low operating temperature, high specific power, and high energy conversion efficiency of PEMFC, it has received more attention in the field of transportation in recent years. However, automotive fuel cells operate in complex working conditions and operating environments, and variable operating conditions will accelerate their performance degradation, making their lifespan much shorter than that of PEMFCs in other fields (such as backup...

AI Technical Summary

Problems solved by technology

[0004] However, the existing data-driven fuel cell remaining life prediction methods still have shortcomings in prediction performance and accuracy: 1) the direct mapping method is difficult to describe the nonlinear decay process of fuel cell performance; 2) signal processing methods, statistical and probability analysis methods It is very sensitive to the quantity and quality of aging data, and the prediction performance is easily disturbed by fluctuations and noise in the measurement data; 4) Although machine learning methods can have nonlinear feature learning capabilities, most methods such as adaptive neuro-fuzzy inference systems , echo state neural network, etc. are all shallow structures, it is difficult to predict highly nonlinear data under uncertain conditions, and it is difficult to distinguish noise and fall into overfitting

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