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

Xiaogui Gou1, Jialing Li2, Bayram Yazdani3,4

  • 1College of intelligent manufacturing, Chongoing Institute Of Engineering, Chongqing, 400056, China.

Scientific Reports
|September 29, 2025
PubMed
Summary

An Improved Grey Lag Goose Optimization (IGLGO) algorithm significantly reduces costs for hybrid locomotive energy systems. This new method optimizes polymer electrolyte membrane fuel cells and lithium-ion batteries for sustainable railway propulsion.

Keywords:
Hybrid energy systemImproved grey lag goose optimizationLithium-ion batteryLocomotivePolymer electrolyte membrane fuel cellTotal cost

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Area of Science:

  • Engineering
  • Optimization Algorithms
  • Sustainable Energy

Background:

  • Hybrid locomotive energy systems require efficient cost optimization.
  • Integrating polymer electrolyte membrane (PEM) fuel cells and lithium-ion batteries presents complex design challenges.
  • Existing optimization algorithms may struggle to avoid local optima in such systems.

Purpose of the Study:

  • To introduce an Improved Grey Lag Goose Optimization (IGLGO) algorithm.
  • To minimize the total cost of hybrid locomotive energy systems.
  • To enhance the exploration-exploitation balance in optimization for better results.

Main Methods:

  • Developed the Improved Grey Lag Goose Optimization (IGLGO) algorithm.
  • Incorporated a dynamic grouping mechanism within IGLGO.
  • Utilized fractional calculus to refine the optimization process.
  • Applied the algorithm to a hybrid system of PEM fuel cells and lithium-ion batteries.

Main Results:

  • The IGLGO algorithm achieved a more cost-effective system design compared to standard Grey Lag Goose Optimization (GLGO) and other metaheuristics.
  • For a 2% track slope, the IGLGO-optimized system cost $3.78 million, significantly lower than GLGO ($4.41 million) and Dwarf Mongoose Optimizer ($4.84 million).
  • Demonstrated avoidance of local optima through a balanced exploration-exploitation strategy.

Conclusions:

  • The IGLGO algorithm provides a robust and economical optimization framework for hybrid locomotive energy systems.
  • This approach is highly effective for sustainable railway propulsion.
  • IGLGO offers a superior alternative for minimizing costs in complex energy system designs.