Water Content Estimation in Polymer Electrolyte Fuel Cells Simplified by Correlating Synchronous Electrochemical Impedance Spectroscopy and Neutron Imaging

Polymer electrolyte fuel cells (PEFCs) play a crucial role in approaching net-zero commitments due to their high-power density, rapid refuelling, and environmentally friendly operation. However, ensuring stable performance and durability relies on subtle water balance. Existing water management control strategies, including humidification, drainage, and cold starts, primarily depend on indirect feedback or calibration through the output voltage. The direct, real-time measurement of the water content inside a PEFC remains challenging, hindering the implementation of an efficient feedback control strategy for water management. To address this issue, synchronous measurement of neutron imaging and electrochemical impedance spectroscopy (EIS) is carried out at various water contents for the first time to the best of our knowledge. Machine learning is used to establish a nonlinear correlation between these two characterisation techniques. This enables the development of a more cost-effective and easily accessible real-time water content estimation technique suitable for high-power applications inferred from a universal EIS characterisation tool rather than relying solely on the limited availability of neutron imaging. This approach facilitates the optimisation and advancement of PEFCs.