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Cold Start Optimization Study of PEMFC Low Temperature Coolant-Assisted Heating Based on CAB-Net and LO-WOA
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DOI：10.3390/hydrogen7010024.png)
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Abstract
En 中文
Proton Exchange Membrane Fuel Cells (PEMFCs) are highly valued for their zero emissions, low noise, and environmentally friendly characteristics. However, they face substantial difficulties when starting up in low-temperature conditions. Coolant-assisted heating is usually more effective than other methods because of its fast speed, high heat transfer efficiency, and simple structure. This study developed a three-dimensional multiphase non-isothermal PEMFC cold start model with coolant-assisted heating. Key parameters, including heat consumption rate, coolant flow rate, load current slope, initial membrane water content, catalyst layer porosity, and gas diffusion layer porosity, were selected as optimization variables. A Convolutional Neural Network-Attention Mechanism-Bidirectional Long Short-Term Memory Neural Network (CAB-Net) was employed as a surrogate model to predict the ice volume fraction during the cold start process. The CAB-Net model was further integrated with the Lexicographic Ordered Whale Optimization Algorithm (LO-WOA) to identify the optimal combination of parameters. The optimization aimed to minimize the maximum ice volume fraction (MIVF) in the Cathode Catalyst Layer (CCL) and reduce the energy consumption required to reach this fraction. The optimization results revealed that, compared to the baseline model (MIVF = 0.4519, energy consumption = 0.77264 J), the MIVF was reduced to 0.1471, representing a 67.45% decrease, while energy consumption was reduced to 0.70299 J, achieving a 9.01% decrease. The results underscore the efficacy of the proposed strategy in enhancing cold start performance under low-temperature conditions.
Keywords:
PEMFC
coolant heating
CAB-Net
LO-WOA
cold start
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