Ultracapacitor-Assisted Energy Management in Hybrid Electric Vehicles using QSNGNN: A Quaternion Similarity-based GNN Approach

An effective Energy Management (EM) system within Hybrid Electric Vehicles (HEVs) with Fuel Cells (FCs) and batteries together with Ultracapacitors (UCs) optimizes power distribution to enhance hydrogen usage. The inefficiency of power distribution leads to enhanced hydrogen usage but leads to control system delays that negatively impact total system performance. To overcome these drawbacks, this manuscript proposes an approach for EM of HEV with FC, battery, and UC. The suggested method is Starfish Optimization Algorithm (SfOA). The main aim of the suggested method is to enhance energy efficiency, reduces hydrogen consumption, and improves the vehicle s overall performance. SFO optimizes power distribution among the FC, battery, and UCs to minimize hydrogen consumption and regulate energy flow efficiently. By then, the suggested approach is implemented in MATLAB and contrasted with several other methods that are previously in use. The suggested technique outperforms all previous techniques such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3PGA), Cheetah Optimizer-Spiking Neural Network (CO-SNN), and Radial Basis Function Neural Network (RBFNN). The SfOA method shows operational effectiveness of 98.7% and consumes 22.71g of hydrogen during operation. The suggested method provides higher energy efficiency and reduced hydrogen consumption which establishes it as an efficient solution for EM in HEVs with hybrid powertrains involving FCs, batteries, and UCs.