Research on Teaching Reform and Optimization of University Fuel Cell Courses Integrated with Machine Learning

Abstract

Driven by the global low-carbon energy transition and China’s dual-carbon strategy, fuel cell technology has become a core compulsory course for energy chemistry, new energy science and engineering, and other related majors in universities. This course features obvious interdisciplinary attributes covering electrochemistry, catalytic materials, fluid mechanics, and automatic control. However, microscopic reaction mechanisms and multiphysics coupling processes are invisible and abstract, bringing great learning difficulties to students. At present, domestic fuel cell teaching faces prominent bottlenecks including fragmented curriculum content, insufficient visual demonstration of microscopic mechanisms, high cost and safety risks of physical experiments, disconnection between teaching and industrial frontiers, and lack of intelligent technology integration. Traditional lecture-based teaching only focuses on theoretical knowledge indoctrination, failing to cultivate students’ data analysis, modeling optimization, and engineering innovation capabilities, which cannot meet the talent demand of intelligent upgrading of the fuel cell industry. Machine learning (ML) has outstanding advantages in nonlinear data fitting, performance prediction, multi-objective optimization, and microscopic mechanism inversion, which provides a new path for fuel cell curriculum reform. Targeting practical teaching pain points of energy majors, this paper summarizes five major teaching dilemmas of existing fuel cell courses, and explores multi-dimensional integration paths of ML and classroom teaching, virtual simulation experiments, project training, and university-enterprise collaborative education. Furthermore, this paper puts forward targeted reform strategies from four aspects: hierarchical knowledge system construction, inquiry-based teaching innovation, practical teaching system expansion, and teacher training and evaluation mechanism optimization. The research results integrate data-driven thinking and intelligent modeling methods into the whole teaching process, effectively solving the problems of abstract mechanism explanation, limited experimental conditions, and lagging frontier technology updates. It provides theoretical support and practical reference for intelligent curriculum reconstruction and innovative engineering talent cultivation of fuel cell courses under an emerging engineering education background.