An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Proton exchange membrane fuel cell (PEMFC) is widely popular for its inherent advantages like low operating temperature, high efficiency, durability, and reliability. However, modelling its characteristics is often restricted by virtue of its shortage of data and strongly coupled multivariate behaviour. At the same time, when operated within its temperature limits, PEMFC systems exhibit maximum efficiency; otherwise, they cause membrane dryness, poor ionic conductivity and less efficiency. Therefore, PEMFC modelling alongside thermal management is of interest in recent years. With the benefits rendered by the metaheuristics algorithm, an integrated approach is emphasised in this work for achieving twin objectives of modelling PEMFC together with thermal management. An aspiration to enhance PEMFC performance via mathematical modelling, flower pollination algorithm for parameter identification as well as thermal conductivity property computation is outlined. Simulated values are experimentally matched using copper‐based nanofluids. The selection of copper (Cu) is justified by its thermal conductivity property and cost. For brevity, synthesis procedure, characterisation and estimation of thermal conductivity property is expounded in methodology. Numerous experimentations on various vol% fractions combinations lying between 0.05% and 0.5% indicate that Cu nanoparticle when dispersed in base fluids such as water and ethylene glycol (EG) substantially improves thermal conductivity property. Especially, among different combinations, nanofluid with EG and water mixture achieves the highest thermal conductivity value of 1.4255.