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<p>This work presents an Improved Grey Lag Goose Optimization (IGLGO) algorithm for minimizing the total cost of a hybrid locomotive energy system integrating polymer electrolyte membrane (PEM) fuel cells with lithium-ion batteries. The IGLGO employs a dynamic grouping mechanism and fractional calculus to ensure that a near-optimal exploration-exploitation balance is achieved so that local optima can be avoided. IGLGO delivered a more cost-effective design as compared to standard Grey Lag Goose Optimization (GLGO) and other metaheuristics. For a 2% track slope, the optimally sized IGLGO system, at $3.78&#xa0;million, was found to be cheaper than GLGO ($4.41&#xa0;million) and the Dwarf Mongoose Optimizer ($4.84&#xa0;million). These results prove that IGLGO offers a very solid yet economical optimization framework for sustainable railway propulsion systems.</p>

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Optimizing hybrid energy systems for locomotives based on improved grey lag goose algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月29日

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  • Xiaogui Gou,
  • Jialing Li,
  • Bayram Yazdani

摘要

This work presents an Improved Grey Lag Goose Optimization (IGLGO) algorithm for minimizing the total cost of a hybrid locomotive energy system integrating polymer electrolyte membrane (PEM) fuel cells with lithium-ion batteries. The IGLGO employs a dynamic grouping mechanism and fractional calculus to ensure that a near-optimal exploration-exploitation balance is achieved so that local optima can be avoided. IGLGO delivered a more cost-effective design as compared to standard Grey Lag Goose Optimization (GLGO) and other metaheuristics. For a 2% track slope, the optimally sized IGLGO system, at $3.78 million, was found to be cheaper than GLGO ($4.41 million) and the Dwarf Mongoose Optimizer ($4.84 million). These results prove that IGLGO offers a very solid yet economical optimization framework for sustainable railway propulsion systems.