Machine Learning-Based Current Density Simulation for Direct Borohydride Fuel Cell

Abstract

Recent studies on fuel cell design have showed that the use of simulation tools is beneficial in terms of saving time and money. Current density management is still a key research problem for several technologies, including Direct Borohydride Fuel Cell (DBFC). This paper describes a systematic machine learning technique for estimating the cell current density for DBFC as a function of various input factors. Artificial Neural Networks (ANN) and Decision Tree Regressor (DTR) are two popular machine learning models that were trained and evaluated for the current density simulation using a conducted fuel cell experiments presented in previous research. The ANN model performed the better than the DTR model in the simulation, with a mean absolute error of 3.00015 for training and 5.57614 for testing. The simulation exhibits very small error values, indicating that the suggested approaches accurately mirror real-world DBFC process.