Extracting optimal fuel cell parameters using dynamic Fick’s Law algorithm with cooperative learning strategy and k-means clustering

Abstract

This article introduces an enhanced stochastic search method tailored for optimizing the parameters of fuel cells (FCs), which hold significant relevance across various applications. The nonlinear nature of FCs poses a modeling challenge, prompting the proposal of an advanced Dynamic Fick’s Law Algorithm (DFLA). This improved approach incorporates a cooperative learning strategy, leveraging K-Means clustering, to derive optimal FC parameters. DFLA introduces a dynamic swarm topology by segmenting the population into subswarms, boosting diversity, and enabling broader global exploration. Simultaneously, the cooperative learning strategy fosters information exchange among subswarms, enhancing the algorithm’s ability to explore vast solution spaces and exploit diverse subswarm-derived solutions. The evaluation of DFLA utilized real-world datasets from commercial PEMFC stacks : 250-W stack, BCS 500-W, and NedStack PS6. Performance assessment relied on the Sum Squared Error (SSE), a standard evaluation metric, with comparisons drawn against established competing methods. The results underscored DFLA’s superior efficiency, showcasing improved performance metrics and convergence behaviors compared to the tested algorithms.