Proton exchange membrane fuel cell aging performance prediction based on conditional neural networks

doi:10.19799/j.cnki.2095-4239.2024.0287

Abstract

Abstract:

In the context of actively pursuing the "dual carbon" goals, the advancement of hydrogen energy has experienced unprecedented opportunities. As a vital component of the green transportation revolution, fuel cell vehicles play a key role in carbon reduction and achieving carbon neutrality. Such vehicles have also become a focus of research on new energy vehicles. Improving the intelligence of fuel cell vehicles and continuously optimizing their performance through machine learning algorithms have become important ways to enhance the efficiency of such vehicles. Proton exchange membrane fuel cells, one of the core technologies of fuel cells, continue to encounter significant challenges in commercialization and market adoption due to durability issues. Accurately predicting the aging performance of fuel cells is highly challenging due to their nonlinearity and dynamic characteristics, coupled with their ever-changing operating conditions. This paper proposes a novel fuel cell performance prediction model based on conditional convolutional neural networks. The proposed model combines linear trends and nonlinear dynamic feature predictions to iteratively forecast the aging performance of fuel cells using a recursive method. The experimental results confirmed the high accuracy of this model in long-term performance prediction, instilling confidence in its practical implications for enhancing the reliability and efficiency of fuel cell systems.

Key words: fuel cell, machine learning, neural network, time series forecasting

CLC Number:

U 469.72

Congxin LI, Meiling YUE, Xintong LI, Qinghui XIONG, Xiaoyan LIU. Proton exchange membrane fuel cell aging performance prediction based on conditional neural networks[J]. Energy Storage Science and Technology, 2024, 13(9): 3094-3102.

Figures/Tables 12

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参数	值
活化面积	33.625 cm²
单电池个数	15
氢气压力	35 kPa
工作温度	75 ℃
最大电流	13.45 A(0.4 A/cm²)

超参数	值
输入层维度	64
卷积层数量	3
卷积核大小	5
卷积和数量	10
池化层大小	2
全连接层神经元数量	128
激活函数	ReLU
优化器	Adam
学习率	0.001
批大小(batch size)	128
训练轮数(epochs)	1000

预测值	RMSE	MAPE
P₇₀₀	0.1013	0.8524
P₁₀₀₀	0.0772	0.6792

预测值	RMSE	MAPE
P₇₀₀	0.2014	1.7213
P₈₀₀	0.1947	1.7201
P₉₀₀	0.1812	1.6624
P₁₀₀₀	0.1729	1.5210

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