MSE Research Project Database

Supercapacitor and battery design through modelling and machine learning


Project Leader: Dan Li
Primary Contact: Dan Li (dan.li1@unimelb.edu.au)
Keywords: machine learning; nanotechnology
Disciplines: Chemical & Biomolecular Engineering,Electrical & Electronic Engineering,Mechanical Engineering
Domains:

Building on Prof Dan Li group’s recent achievements in synthesis of structurally tuneable graphene-based gel membranes and our ability to quantify their microstructure, this project will build a new equivalent electric circuit (EEC) tool to establish a quantitative structure-performance relationship of supercapacitors, which will then be used to design novel electrode and device structures to realise supercapacitors with high usable energy storage capacity at high operation rates. The specific objectives are to:

(i) Using graphene membranes as model electrodes and through a combination of electrochemical and nanofluidic experiments with computer simulations and machine learning, establish a new EEC model to describe the whole supercapacitor cell in which the effects of the electrode structure (specific surface area, pore size, electrode thickness), electrolytes (different anion/cation combinations) and the charging rate are all correlated quantitatively.

(ii) Using the new EEC model to illustrate how the gradient of electrode porosity interplays with other structural and operational parameters and the device configuration, and investigate how these parameters can be optimised collectively to realise high energy storage and power delivery capacity.

(iii) Develop new techniques to fabricate graded/asymmetric electrode structures to realise the optimised design experimentally.

This approach will be extended to design batteries and fuel cells.

Representative publications from Li’s group on this research theme:

  1. Yang, X., Cheng, C., Wang, Y. Qiu, L. & Li, D, Liquid-mediated dense integration of graphene materials for compact capacitive energy storage, Science, 341, 534-537 (2013).
  2. Cheng, C., Jiang, G., Garvey, C. J., Wang, Y., Simon, G. P., Liu, J. Z. & Li, D.Ion transport in complex layered graphene-based membranes with tuneable interlayer spacing, Science Advances, 2, e1501272 (2016)