Electrochemical Finite Element Modelling to Assist Development of Machine-Learning Tools for Battery Ageing Modelling swegrids-logo

SweGRIDS research area Materials for Power Grid and Storage
SweGRIDS project code MTL9
Project type PostDoc
Status running
Researcher Litao Yin   (webpage)
University UU
Project period 2021-08-01 to 2021-12-31   
Project supervisor Daniel Brandell   (webpage)
Industrial sponsors ABB

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Project abstract

Two trends in Li-ion research are currently developing at a rapid speed: (1) using finite element methods (FEM) to estimate lifetime, ageing mechanisms, etc., and (2) using machine-learning (ML)/artificial intelligence (AI) tools for analyzing large data sets of battery test data, and thereby creating models for battery ageing depending on use conditions. This project regards the combination of these two approaches, to assist training and validating the AI, and rendering the ML models better transparent in terms of physical description. Thereby, they can be used for improved battery design. Battery FEM models will thus be employed in this project to generate a much larger matrix of simulated battery ageing data, which the ML model will be validated against. Moreover, the FEM data sets generated will aid in determining which ageing mechanism that is dominating under what battery operating condition, and why.

Summary of work


Event log


Project reference-group

Prof. Kristina Edström,  Uppsala University
Dr. Cathy Yao Chen,  ABB
Dr. Shiva Sander-Tavallaey,  ABB

Publications by this researcher

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Analyzing and mitigating battery ageing by self-heating through a coupled thermal-electrochemical model of cylindrical Li-ion cells
Litao Yin,   Are Björneklett,   Elisabeth Söderlund,   Daniel Brandell.
2021,   Journal of Energy Storage, vol. 39

Publication list last updated from DiVA on 2022-04-16 02:36.

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Page started: 2021-08-01
Last generated: 2022-04-16