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<p>In this paper, a real-time optimized sigmoid-based energy management system (EMS) is proposed for a fuel cell hybrid vehicle (FCHV). The power demand is dynamically shared among the fuel cell, a lithium-ion battery (LIB), and an ultracapacitor. The sigmoid function is employed to regulate the full cell operation, ensuring higher efficiency. A real-time particle swarm optimization (RT-PSO) algorithm defines both the power distribution and the ultracapacitor frequency response in a multi-objective framework that accounts for source degradation, fuel consumption, and power losses, without relying on prior knowledge of the driving cycle. To reduce computational complexity, the optimization is executed at fixed time steps. Numerical results demonstrate the enhanced performance and real-time feasibility of the proposed strategy, increasing LIB lifetime by factors of 1.41 and 1.74 compared to global and non-optimized strategies, respectively, while reducing operational costs by 16.5% and 27.4%. These results confirm that the proposed EMS is a suitable real-time solution with optimal performance and low computational burden.</p>

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Real-Time Optimization of a Sigmoid-Based Energy Management System for a Fuel Cell Hybrid Vehicle

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Lucas Jonys Ribeiro Silva,
  • Márcio Von Rondow Campos,
  • Thales Augusto Fagundes,
  • Deniver Reinke Schutz,
  • Rodolpho Vilela Alves Neves,
  • Ricardo Quadros Machado,
  • Vilma Alves de Oliveira

摘要

In this paper, a real-time optimized sigmoid-based energy management system (EMS) is proposed for a fuel cell hybrid vehicle (FCHV). The power demand is dynamically shared among the fuel cell, a lithium-ion battery (LIB), and an ultracapacitor. The sigmoid function is employed to regulate the full cell operation, ensuring higher efficiency. A real-time particle swarm optimization (RT-PSO) algorithm defines both the power distribution and the ultracapacitor frequency response in a multi-objective framework that accounts for source degradation, fuel consumption, and power losses, without relying on prior knowledge of the driving cycle. To reduce computational complexity, the optimization is executed at fixed time steps. Numerical results demonstrate the enhanced performance and real-time feasibility of the proposed strategy, increasing LIB lifetime by factors of 1.41 and 1.74 compared to global and non-optimized strategies, respectively, while reducing operational costs by 16.5% and 27.4%. These results confirm that the proposed EMS is a suitable real-time solution with optimal performance and low computational burden.