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State of Health Estimation for Proton Exchange Membrane Fuel Cells Based on Particle Filtering Algorithm
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Jianhua GAO1, Su ZHOU1, 2, Qi SUN1, Peng ZHAO1, Lei FAN2, Wei SHEN1, 2
Chinese Journal of Automotive Engineering | 2024, 14(4) : 622 - 630
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Chinese Journal of Automotive Engineering | 2024, 14(4): 622-630
Technology and Research
State of Health Estimation for Proton Exchange Membrane Fuel Cells Based on Particle Filtering Algorithm
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Jianhua GAO1, Su ZHOU1, 2, Qi SUN1, Peng ZHAO1, Lei FAN2, Wei SHEN1, 2
Affiliations
  • 1 School of Automotive Studies Tongji University Shanghai 201804 China
  • 2 School of Intelligent Manufacturing Shanghai Zhongqiao Vocational and Technical University Shanghai 201514 China
doi: 10.3969/j.issn.2095-1469.2024.04.06
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The aging process of a proton exchange membrane fuel cell (PEMFC) affects its output performance, and in order to accurately control output power, it is necessary to consider the aging and power degradation trends of the PEMFC. In this paper, the powercurrent curve is used as an indicator of the state of health (SOH). Based on previous studies, improvements have been made by considering changes in opencircuit voltage during the aging process. The number of aging factors in the aging model has been increased and the mapping relationship between the PEMFC power and the aging of the internal components is established. A semimechanical power degradation model is derived based on polarization curves, and an aging rate model has been designed using the particle filter algorithm. Combining the power decay analysis, the paper estimated the fuel cell's SOH. Simulations were carried out on the test dataset and compared with experimental test data. The results show that the method can predict the longterm performance decay model. Furthermore, compared with existing research methods, the proposed method estimates the SOH and performance decay trend of PEMFCs more accurately through the use of aging rate reference values and the power decay model. With reduced training time, there is an improvement in estimation accuracy. Especially when the training time is 100 hours and the estimation time is 250 hours, the error's relative decrease rate reaches 65.69%.

fuel cell  /  aging  /  state of health estimation  /  particle filter
Jianhua GAO, Su ZHOU, Qi SUN, Peng ZHAO, Lei FAN, Wei SHEN. State of Health Estimation for Proton Exchange Membrane Fuel Cells Based on Particle Filtering Algorithm[J]. Chinese Journal of Automotive Engineering, 2024 , 14 (4) : 622 -630 . DOI: 10.3969/j.issn.2095-1469.2024.04.06
Year 2024 volume 14 Issue 4
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doi: 10.3969/j.issn.2095-1469.2024.04.06
  • Receive Date:2023-08-20
  • Online Date:2025-07-20
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  • Received:2023-08-20
  • Revised:2023-09-23
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    1 School of Automotive Studies Tongji University Shanghai 201804 China
    2 School of Intelligent Manufacturing Shanghai Zhongqiao Vocational and Technical University Shanghai 201514 China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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