This technology includes a monitoring architecture, system and sensor residual life prediction AI technology, and specification sizing technology for a fuel cell vehicle. The fuel cell system monitoring architecture proposes plans for potential risks, detection methods, and corresponding mechanisms which can be used to design sensing or detection logic. The residual life AI prediction technology collects data on the system operation and environmental conditions to predict subsequent performance, which can be compared with the current system operation results to detect anomalies in advance, avoiding accelerated system failure due to erroneous detection or performance issues.

AI Technology for Predicting Residual Life of Fuel Cell Systems and Sensors 

1.Input Signal Types: Fuel cell system output voltage (V)/current (A), hydrogen supply pressure (bar), ambient temperature (°C), ambient humidity (%), hydrogen sensor sensing voltage (or current) and detected values. 

2.AI Model Computing Environment Requirements: Python programming software, general PC.

This technology can be applied to devices or vehicles that use hydrogen energy systems as a power source.

This technology can assist domestic manufacturers who develop fuel cell system applications in establishing safety monitoring technology, improving system safety and real-time response capabilities. At the same time, it can help automakers reduce time and cost in the development of fuel cell vehicles, accelerating the promotion of future hydrogen energy vehicles.