Systems and methods for monitoring a fuel cell

An apparatus to monitor a fuel cell system of a vehicle, the apparatus comprising:

• a processor operatively coupled to a memory;
• a monitoring module configured for operation on the processor, the monitoring module configured to:
• determine a first metric configured to track a specified operational characteristic of the fuel cell system, the first metric derived from first monitoring data comprising measurements of the specified operational characteristic, and
• determine a second metric configured to track the specified operational characteristic of the fuel cell system, the second metric comprising estimates of the specified operational characteristic derived from second monitoring data having a defined physical relationship to the specified operational characteristic;
• a feature extraction module configured to generate features corresponding to a machine-learned model (ML) from the first metric and the second metric, the features corresponding to a rate of change of the first metric and a rate of change of the second metric;
• an analysis module comprising the ML model, the analysis module configured to trigger detection of a fuel leak based, at least in part, on a leak prediction determined for the fuel cell system by the ML model, the leak prediction based on the features generated from the first metric and the second metric, the features indicating a difference between the rate of change of the first metric and the rate of change of the second metric at respective times within the monitoring period;
• and
• a mitigation module configured to implement a mitigation action at the vehicle in response to detection of the fuel leak, the mitigation action comprising closing a valve of the fuel cell system.

A non-transitory computer-readable storage medium comprising instructions configured to cause a processor of a computing device to implement operations for monitoring a fuel cell system of a vehicle, the operations comprising:

• deriving a first metric from first monitoring data, the first metric configured to track fuel consumed by the fuel cell system, the first monitoring data comprising measurements of one or more of a temperature of fuel stored within fuel storage of the fuel cell system, a pressure of the fuel, a mass of the fuel, an inflow of fuel to the fuel cell system, and an outflow of fuel from the fuel cell system;
• deriving a second metric from second monitoring data, the second metric comprising estimates of the fuel consumed by the fuel cell system derived from indirect measurements of the second monitoring data and power efficiency data pertaining to the fuel cell system, the indirect measurements comprising one or more of a power output of the fuel cell system and power supplied to a load coupled to the fuel cell system;
• generating features corresponding to a machine-learned (ML) model from the first metric and the second metric, the features corresponding to a rate of change of the first metric and a rate of change of the second metric;
• configuring the ML model to determine a leak prediction for the fuel cell system based, at least in part, on the features generated from the first metric and the second metric, the features indicating a difference between the rate of change of the first metric and the rate of change of the second metric at respective times within the monitoring period;
• triggering detection of a leak in the fuel cell system based, at least in part, on the leak prediction determined by the ML model;
• and
• implementing a mitigation action at the vehicle in response to detection of the leak in the fuel cell system, the mitigation action comprising closing a valve of the fuel cell system.