Novel Neural network single sensor MPPT for Proton Exchange Membrane Fuel Cell

This paper presents a new neural network single sensor maximum power point tracking algorithm controlling the DC-DC boost converter to guarantee the transfer of the proton exchange membrane fuel cell maximum generated power to the load. The implemented neural network single sensor controller has been developed and trained firstly in offline mode using single sensor maximum power point tracking data obtained previously; and secondly used in online mode to track the maximum output power of the fuel cell power system. Comparative simulation results prove the superiority of the proposed neural network single sensor maximum power point compared to the single sensor one especially in transit response reducing by the way the overshoot and the tracking time which leads to an overall energy losses reduction. In addition, the implemented neural network single sensor MPPT employs only one sensor which will reduce the complexity and the cost of PEM fuel cell power system. To our knowledge, this study is a pioneering work using a neural network single sensor controller as PEM fuel cell MPPT.

Automated summary

This study introduces a novel neural network single sensor maximum power point tracking algorithm to optimize the output power of proton exchange membrane fuel cells. Comparative simulation results demonstrate the superiority of the proposed neural network controller over traditional single sensor methods in terms of transit response reduction and overall energy losses. Moreover, the implementation of the neural network controller with only one sensor reduces complexity and cost in PEM fuel cell power systems.