Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm

Zerong Huang, Daxing Zhang, Xiangdong Wang, Xiaolong Huang, Chunsheng Wang, Liqing Liao, Yaolin Dong, Xiaoshuang Hou, Yuan Cao and Xinyao Zhou ()
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Zerong Huang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Daxing Zhang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Xiangdong Wang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Xiaolong Huang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Chunsheng Wang: School of Automation, Central South University, Changsha 410083, China
Liqing Liao: School of Automation, Central South University, Changsha 410083, China
Yaolin Dong: School of Automation, Central South University, Changsha 410083, China
Xiaoshuang Hou: School of Automation, Central South University, Changsha 410083, China
Yuan Cao: School of Automation, Central South University, Changsha 410083, China
Xinyao Zhou: School of Automation, Central South University, Changsha 410083, China

Mathematics, 2024, vol. 12, issue 19, 1-16

Abstract: In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Keywords: proton exchange membrane fuel cells; variational mode decomposition; back propagation neural network; degradation prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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