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The rapid adoption of electric vehicles (EVs) has increased the need for efficient hybrid energy management (HEM) techniques to optimize power distribution, improve battery life, and enhance vehicle performance. Hybrid energy management integrates multiple energy sources, such as batteries, fuel cells, and supercapacitors, to ensure a reliable and sustainable power supply. However, real-time scheduling of these energy sources remains a critical challenge due to dynamic driving conditions, varying energy demands, and computational complexity. This paper explores an efficient scheduling technique for HEM in EVs, leveraging optimization algorithms, machine learning, and predictive control strategies to allocate energy dynamically based on real-time factors. The study evaluates existing scheduling methods, highlights their limitations, and proposes an intelligent and adaptive framework to enhance energy efficiency. to be considered in the design is calculated and a design method is proposed to ensure the stability of DC microgrid in all operating modes.