Multiple subpopulation Salp swarm algorithm with Symbiosis theory and Gaussian distribution for optimizing warm-up strategy of fuel cell power system

Swarm optimization algorithms have become a research hotspot for solving multiple parameter optimization problems in fuel cell systems to enhance hydrogen usage efficiency. However, the startup warming strategy is highly complex due to stage-wise constraints, resulting in slow convergence and suboptimal outcomes with conventional algorithms. Given that, this work innovatively proposes a multiple subpopulation division mechanism. It introduces symbiosis theory and Gaussian distribution to improve the basic Salp swarm algorithm, enhancing its local search and global exploitation capabilities when considering multiple constraints. First, a startup-warming model is developed to characterize the fuel cell temperature variation patterns during the energy conversion. Then, the optimization objective function is constructed, incorporating complex stage-wise restrictive conditions to reveal the energy consumption mechanism and identify the limiting factors of the warming strategy. Finally, the improved Salp swarm algorithm facilitates the efficient and reliable identification of the optimal warming strategy to minimize energy consumption. Experimental results demonstrate that compared to the basic algorithm, the proposed method reduces energy consumption by up to 6.41 %, 4.25 %, and 4.88 % under startup duration, initial temperature, and target temperature constraints. The multiple subpopulation Salp swarm algorithm demonstrates excellent performance and significant advantages in optimizing fuel cell energy efficiency.