Adaptive Dynamic Power Allocation for Enhanced Performance in Fuel Cell-Supercapacitor OEWIM Systems

With environmental pollution widespread and a critical shortage of fossil fuels, the development of electric vehicles powered by green energy sources becomes crucial for tackling this issue. Especially noteworthy is the hybrid electric vehicle that utilizes fuel cells and supercapacitors, which is the most effective option. Numerous optimization challenges exist in finding power sharing algorithms between fuel cell (FC) and supercapacitor (SC) aimed to maximize the system's efficiency while upholding the supercapacitor's state of charge (SOC) boundaries. However, most of the existing models don't consider the FC dynamic impact caused by several factors such as FC membrane hydration, temperature, and hydrogen pressure which can affects negatively on performance of the system. The paper presents inner loop control algorithm that aims to manage the power-sharing between FC and SC sources guided by the decision input power sharing coefficient. Additionally, the paper shows an outer loop control algorithm that performs two functions: the 1<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> is to identify the FC model specifically the curve that links the efficiency to the output power of the FC. The 2<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> seeks to optimize both overall efficiency and the operational management of the supercapacitor's SOC. These objectives are integrated within the suggested adaptive multiple prediction horizon dynamic power-sharing model predictive control (MPC) algorithm and utilized for an open-end winding induction motor (OEWIM) fed by the power sources through a dual traditional inverter.