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An advanced 1D physics-based model for PEM hydrogen fuel cells with enhanced overvoltage prediction
This quantity replaces the limit current density coefficient ($i_{lim}$) and establishes a connection between the voltage drop at high current densities, the amount of liquid water present in the catalyst layers of the cell, and the operating conditions.
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning
In the paper, a fuzzy reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel consumption, maintain the batteries' long-term operation, and extend the lifetime of the fuel cells system.
Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load
Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC).
