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<p>In recent years, energy management strategies for fuel cell electric vehicles have received great attention due to the need for efficient power distribution and better fuel economy. This study proposes a hybrid energy storage system composed of a fuel cell as the main source, supported by a lithium-ion battery and a supercapacitor. The main objective is to improve fuel cell efficiency and maintain the DC Bus voltage stability under different driving conditions. A deep neural network-based power management strategy was developed using MATLAB and trained with a large dataset to optimize power sharing between the components. Simulation results show that the proposed method satisfies load demand accurately, reduces fuel cell power fluctuations, and keeps the DC Bus voltage within ±0.61% of its nominal value. The results confirm improved energy efficiency, enhanced fuel cell lifetime, and better system stability. This work presents a practical and intelligent solution suitable for real-time energy management in fuel cell electric vehicles.</p>

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Power Management Strategy for Hybrid Energy Storage in Electric Vehicles Using Deep Neural Network Considering DC Bus Stabilization and Fuel Cell Power Economy

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Younes Djaballah,
  • Karim Negadi,
  • Mohamed Boudiaf

摘要

In recent years, energy management strategies for fuel cell electric vehicles have received great attention due to the need for efficient power distribution and better fuel economy. This study proposes a hybrid energy storage system composed of a fuel cell as the main source, supported by a lithium-ion battery and a supercapacitor. The main objective is to improve fuel cell efficiency and maintain the DC Bus voltage stability under different driving conditions. A deep neural network-based power management strategy was developed using MATLAB and trained with a large dataset to optimize power sharing between the components. Simulation results show that the proposed method satisfies load demand accurately, reduces fuel cell power fluctuations, and keeps the DC Bus voltage within ±0.61% of its nominal value. The results confirm improved energy efficiency, enhanced fuel cell lifetime, and better system stability. This work presents a practical and intelligent solution suitable for real-time energy management in fuel cell electric vehicles.