Robust Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on the RPO-DTRN Framework

Fuel Cell Hybrid Electric Vehicles (FCHEVs) present a promising solution for sustainable trans�portation, but their widespread usage is limited by challenges like fuel cell stack degradation and
inefficient power distribution. Conventional energy management strategies have difficulty in bal�ancing power allocation efficiently while maximizing the fuel cell lifespan. To overcome these
constraints, this research introduces a novel hybrid methodology-based energy management strat�egy for FCHEVs to enhance the fuel cell stack’s lifetime. The proposed framework integrates the Red
Panda Optimization (RPO) algorithm with a Dual Transformer Residual Network (DTRN), referred
to as the RPO-DTRN approach. The RPO algorithm optimizes power distribution in hybrid elec�tric vehicles, ensuring efficient energy utilization, while the DTRN model predicts and improves
the remaining useful life of the fuel cell stack. The proposed strategy is evaluated using MATLAB
environment and compared against existing techniques, including the Soft Actor-Critic algorithm,
Twin Delayed Deep Deterministic Policy Gradient algorithm and Harris Hawks Optimization-Sine
Cosine algorithm. Results demonstrate that the proposed RPO-DTRN method outperforms the exist�ing methods, attaining a high efficiency of 97% and the lowest error value of 0.148. Additionally,
the proposed strategy minimizes hydrogen consumption to 87.28 g, making it a more fuel-efficient
approach. Furthermore, it achieves the highest final state of charge at 79.04%, indicating superior
battery energy retention and overall system sustainability. These findings suggest the efficiency of
the proposed energy management method in improving the reliability, efficiency, and longevity of
FCHEVs.