4.7 Article

Prediction of local current distribution in polymer electrolyte membrane fuel cell with artificial neural network

Journal

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 46, Issue 39, Pages 20678-20692

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2021.03.168

Keywords

Polymer electrolyte membrane fuel; cell; Neural network; Local current distribution; Segmented fuel cell; Fuel cell model

Funding

  1. Institutie of Advanced Machinery and Design (IAMD) of Seoul National University
  2. Institutie of Engineering Research (IER) of Seoul National University
  3. Brain Korea 21 Project of the Ministry of Education in 2020 [F14SN02D1310]
  4. Korea Institute of Energy Technology Evaluation and Planning (KETEP) from the Ministry of Trade, Industry & Energy of Korea [20173010032150]
  5. Basic Science Research Program through the National Research Foundation (NRF) - Ministry of Science, ICT & Future Planning [2019R1A2C2087893]
  6. Korea Evaluation Institute of Industrial Technology (KEIT) [20173010032150] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  7. National Research Foundation of Korea [2019R1A2C2087893] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Developers in the fuel cell field often use segmented fuel cells to observe current distribution and collect experimental data, which is then utilized to develop a local current prediction model. With the use of a neural network-based model, the study was able to predict local current values with only a 2.98% error. Additionally, an optimal operating condition was determined to achieve uniform current distribution, with a standard deviation of 0.039 A/cm(2) achieved under a current load of 1 Acm(2).
In the development process of a fuel cell, understanding the local current distribution is essentially required to achieve better performance and durability. Therefore, many developers apply a segmented fuel cell to observe current distribution under various operating conditions. With the application, experimental data is collected. This study suggests a utilization method for this collected data to develop a local current prediction model. The details of this neural network-based prediction model are introduced, including the pretreatment of the data. In the pretreatment process, current residual values are used for better prediction performance. As a result, the model predicted local current values with a 2.98% error. With the model, the effects of pressure, temperature, cathode relative humidity, and cathode flow rate on local current distribution trends are analyzed. Since the non-uniform current distribution of a fuel cell often leads to low performances or fast local degradation, the optimal operating condition to achieve current uniformity is acquired with an additional model. This model is developed by switching inputs and outputs of the local current prediction model. With the model application, the uniform current distribution is achieved with a standard deviation of 0.039 A/cm(2) under the current load at 1 Acm(2). (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

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