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<p>Hydrogen fuel cell trains, which use both fuel cells and battery systems, require advanced technologies for power distribution and state-of-charge (SOC) management—unlike conventional electric trains powered by a single overhead power source. Such an energy management strategy (EMS) aims to reduce energy consumption and lower life-cycle operating costs by extending the service life of energy sources. This study presents an energy management strategy for a hydrogen fuel cell train. The equivalent factor is defined as a variable that depends on vehicle operating conditions. Based on this, the Equivalent Consumption Minimization Strategy (ECMS) is applied to determine the optimal operating point that minimizes hydrogen consumption. Furthermore, Model Predictive Control (MPC) is employed to prolong the service life of the energy sources by regulating system operation. The proposed algorithm is implemented in MATLAB/Simulink and validated through simulations coupled with a vehicle model.</p>

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A Study on Energy Optimization Strategy for Hydrogen Fuel Cell Train Using ECMS with Model Predictive Control

  • 期刊论文

  • 发表日期：

    2025年9月16日

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  • Yongkuk Oh,
  • Joonhyoung Ryu,
  • Jaewon Kim,
  • Hyeongcheol Lee

摘要

Hydrogen fuel cell trains, which use both fuel cells and battery systems, require advanced technologies for power distribution and state-of-charge (SOC) management—unlike conventional electric trains powered by a single overhead power source. Such an energy management strategy (EMS) aims to reduce energy consumption and lower life-cycle operating costs by extending the service life of energy sources. This study presents an energy management strategy for a hydrogen fuel cell train. The equivalent factor is defined as a variable that depends on vehicle operating conditions. Based on this, the Equivalent Consumption Minimization Strategy (ECMS) is applied to determine the optimal operating point that minimizes hydrogen consumption. Furthermore, Model Predictive Control (MPC) is employed to prolong the service life of the energy sources by regulating system operation. The proposed algorithm is implemented in MATLAB/Simulink and validated through simulations coupled with a vehicle model.