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Remaining Useful Life Prediction of Vehicular Fuel Cells Based on GWO-RBF Neural Network
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Wen WANG1, Han ZHANG2, Bo ZHANG2, Bin LI1, Ji-bin YANG3, *, Le WANG3, 4
Science Technology and Engineering | 2025, 25(14) : 5897 - 5904
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Science Technology and Engineering | 2025, 25(14): 5897-5904
• Papers·Electrical Technology •
Remaining Useful Life Prediction of Vehicular Fuel Cells Based on GWO-RBF Neural Network
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Wen WANG1, Han ZHANG2, Bo ZHANG2, Bin LI1, Ji-bin YANG3, *, Le WANG3, 4
Affiliations
  • 1. CRRC Datong Co., Ltd., Datong 037038, China
  • 2. CRRC Academy Co., Ltd., Beijing 100070, China
  • 3. School of Automobile and Transportation, Xihua University, Chengdu 610039, China
  • 4. School of Intelligent Connected and New Energy Vehicles, Geely University of China, Chengdu 641423, China
Published: 2025-05-18 doi: 10.12404/j.issn.1671-1815.2405154
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In order to study the prediction and health management of PEMFCs(proton exchange membrane fuel cells) for vehicles, a method combining GWO(grey wolf optimizer) and RBF(radial basis function) neural network with relative power loss rate as a health indicator was proposed to predict the remaining useful life of vehicular PEMFCs. Firstly, by analyzing the polarization curve of the fuel cell at the initial moment, a calculation method based on the relative power loss rate as a health indicator was constructed, and its feasibility was verified using the grey correlation analysis method. Then, the RBF neural network optimized by GWO algorithm was applied to predict the remaining useful life of vehicular PEMFCs. Finally, the proposed method was validated using two datasets. The results show that compared with other methods, the GWO-RBF method proposed in this paper has the smallest average absolute percentage error and root mean square error, the largest coefficient of determination, and a relative error of less than 1%. It is concluded that the proposed method can be used to predict the remaining useful life of vehicular PEMFCs with fewer datasets and better accuracy.

fuel cell  /  life prediction  /  relative power loss rate  /  grey wolf optimizer algorithm  /  radial basis function neural network
Wen WANG, Han ZHANG, Bo ZHANG, Bin LI, Ji-bin YANG, Le WANG. Remaining Useful Life Prediction of Vehicular Fuel Cells Based on GWO-RBF Neural Network[J]. Science Technology and Engineering, 2025 , 25 (14) : 5897 -5904 . DOI: 10.12404/j.issn.1671-1815.2405154
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Year 2025 volume 25 Issue 14
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Article Info
doi: 10.12404/j.issn.1671-1815.2405154
  • Receive Date：2024-07-10
  • Online Date：2025-07-09
  • Published：2025-05-18
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History
  • Received：2024-07-10
  • Revised：2025-02-27
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Affiliations
    1. CRRC Datong Co., Ltd., Datong 037038, China
    2. CRRC Academy Co., Ltd., Beijing 100070, China
    3. School of Automobile and Transportation, Xihua University, Chengdu 610039, China
    4. School of Intelligent Connected and New Energy Vehicles, Geely University of China, Chengdu 641423, China
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