TY - JOUR
T1 - Interval prediction of fuel cell degradation based on voltage signal frequency characteristics with TimesNet-GPR under dynamic conditions
AU - Zhu, Wenchao
AU - Li, Yongjia
AU - Xu, Yafei
AU - Zhang, Leiqi
AU - Guo, Bingxin
AU - Xiong, Rui
AU - Xie, Changjun
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Proton exchange membrane fuel cells (PEMFCs) are crucial modern sustainable energy generation devices. The accurate assessment of their state of health (SOH) and the forecast of their remaining useful life (RUL) are critical for their practical deployment. Current mainstream methods typically use time-domain voltage decay as the health indicator (HI) and rely on recurrent neural networks. However, PEMFC voltage decay results from multiple factors, including internal component degradation, changes in operating conditions, and environmental impacts. Low-frequency domain analysis can effectively detect degradation in the proton exchange membrane and gas diffusion layer, leading to more accurate SOH estimation for fuel cells. This study reshapes time-domain voltage signals into frequency factors in a 2D space based on frequency domain features to more accurately reflect the aging characteristics of PEMFCs. We propose a TimesNet-GPR method to accurately quantify the uncertainty in degradation prediction, demonstrating good adaptability with different lengths of training data and various dynamic conditions. This method uses TimesNet for point estimation prediction, overcoming the limitations of neural networks in capturing long-term dependencies, improving point estimation accuracy by 39.18%–70.14% on dynamic cycling condition datasets. In order to evaluate uncertainty during point estimation and provide more accurate confidence interval predictions, Gaussian Process Regression (GPR), is utilized.
AB - Proton exchange membrane fuel cells (PEMFCs) are crucial modern sustainable energy generation devices. The accurate assessment of their state of health (SOH) and the forecast of their remaining useful life (RUL) are critical for their practical deployment. Current mainstream methods typically use time-domain voltage decay as the health indicator (HI) and rely on recurrent neural networks. However, PEMFC voltage decay results from multiple factors, including internal component degradation, changes in operating conditions, and environmental impacts. Low-frequency domain analysis can effectively detect degradation in the proton exchange membrane and gas diffusion layer, leading to more accurate SOH estimation for fuel cells. This study reshapes time-domain voltage signals into frequency factors in a 2D space based on frequency domain features to more accurately reflect the aging characteristics of PEMFCs. We propose a TimesNet-GPR method to accurately quantify the uncertainty in degradation prediction, demonstrating good adaptability with different lengths of training data and various dynamic conditions. This method uses TimesNet for point estimation prediction, overcoming the limitations of neural networks in capturing long-term dependencies, improving point estimation accuracy by 39.18%–70.14% on dynamic cycling condition datasets. In order to evaluate uncertainty during point estimation and provide more accurate confidence interval predictions, Gaussian Process Regression (GPR), is utilized.
KW - Frequency factors
KW - Fuel cell
KW - Interval estimations
KW - TimesNet
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85212813409&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2024.144503
DO - 10.1016/j.jclepro.2024.144503
M3 - Article
AN - SCOPUS:85212813409
SN - 0959-6526
VL - 486
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 144503
ER -