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Jiashen Chen

Profile

I am a doctoral candidate in the class of 2024 at the School of Mechanical Engineering, University of Leeds, under the supervision of Dr Meisam Babaie. I received my B.Eng. from the School of Materials Science and Engineering at the University of Science and Technology Beijing (China) in 2021. Subsequently, I was awarded an M.Sc. from the College of Engineering and Physical Sciences at the University of Birmingham (UK) in 2023, where my research focused on the application of finite-element simulation techniques to the performance and design optimisation of machinery. As a member of the Advanced Modelling and Process Simulation (AMPS) research group at Leeds, I collaborate with multidisciplinary teams to leverage state-of-the-art computational methods in addressing critical challenges in lithium-ion battery development.

Research interests

My research centres on the development and integration of advanced computational models: discrete element models (DEM), finite element models (FEM) and machine learning frameworks, to elucidate the coupled mechanical and electrochemical phenomena in all-solid-state batteries (ASSB) electrodes during high-pressure calendering and assembly. Specifically, I aim to:

  • Construct high-fidelity DEM simulations to capture particle-particle and particle-binder interactions under varying compaction pressures, enabling prediction of electrode microstructure evolution.

  • Enhance FEM analyses to model electrochemical phenomena, such as ion diffusion, electric potential distribution and reaction kinetics, within the electrode stack during calendering and high‑pressure assembly, thereby elucidating how process parameters influence cell performance metrics (capacity, rate capability and cycle stability).

  • Apply machine learning algorithms for rapid surrogate modelling of electrode behaviour, facilitating real-time optimisation of process conditions and materials selection.

Through this integrated modelling approach, my goal is to advance the fundamental understanding of how manufacturing processes influence electrode architecture and, ultimately, battery performance metrics such as capacity retention, rate capability and cycle life. I am committed to contributing computational insights that drive the next generation of high energy density, durable lithium-ion cells.