Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within relatively narrow range of temperature humidity to achieve optimal performance; however, this makes the difficult control, leading faults accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but analysis, or advanced characterisation that more accurate diagnosis often requires invasive slow measurements. To provide acquisition, machine learning methods have shown great potential. However, there is broad approach diagnostic algorithms signals in field. This article critical view current suggests recommendations future methodologies cell applications.