Exploration and practice of an elite innovative graduate training model in the field of green energy based on the triadic integration of ‘Experimentation-Simulation-Algorithms’
Discipline Construction of Energy Storage|更新时间:2026-07-13
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Exploration and practice of an elite innovative graduate training model in the field of green energy based on the triadic integration of ‘Experimentation-Simulation-Algorithms’
Energy Storage Science and TechnologyVol. 15, Issue 6, Pages: 2421-2429(2026)
KANG Zhenye, MAO Sihan, LIU Yun, et al. Exploration and practice of an elite innovative graduate training model in the field of green energy based on the triadic integration of 'Experimentation-Simulation-Algorithms'[J]. Energy Storage Science and Technology, 2026, 15(6): 2421-2429.
KANG Zhenye, MAO Sihan, LIU Yun, et al. Exploration and practice of an elite innovative graduate training model in the field of green energy based on the triadic integration of 'Experimentation-Simulation-Algorithms'[J]. Energy Storage Science and Technology, 2026, 15(6): 2421-2429.DOI: 10.19799/j.cnki.2095-4239.2025.1146.
Exploration and practice of an elite innovative graduate training model in the field of green energy based on the triadic integration of ‘Experimentation-Simulation-Algorithms’
This paper aims to address the urgent demand for high-level talents in green energy technologies under the carbon peaking and neutrality goals and address prominent problems in traditional engineering postgraduate education
such as rigid disciplinary barriers
disconnection between theory and practice
and insufficient innovation capability. To this end
it proposes and implements a novel triadic integration training "experimentation–simulation–algorithm" for postgraduate students. Considering proton exchange membrane water electrolysis
a national strategic frontier
as an example
this model is driven by complex real-world engineering challenges. Through a four-in-one implementation path comprising curriculum restructuring
multisupervisor collaborative guidance
closed-loop iterative training
and industry–education integration support
the algorithm guides students toward integrating three core knowledge modules: materials engineering
multi-physics simulation
and intelligent optimization algorithms
thereby forming a closed research loop of "rational design–precision fabrication–efficient verification." An analysis of the model's implementation reveals that it significantly stimulates students' drive for intrinsic innovation. Graduates achieved outstanding academic outcomes and developed "convergent design" capabilities and systems-based thinking to solve complex engineering and scientific problems. This study proposes and tests a replicable and scalable practical paradigm for cultivating high-quality innovative talents in interdisciplinary fields such as energy storage and hydrogen energy in the "Emerging Engineering Education" context
offering an important reference for promoting connotative development of engineering education and therefore serving major national strategic needs.
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