Li-ion battery diagnostics through combined cell modelling and electroanalytical tools

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Project abstract

In order to optimize the battery usage, i.e. by optimizing charge/discharge rates and keeping within an optimal voltage window, and hence minimize battery ageing, it is necessary to obtain a diagnosis of the battery in terms of what is normally referred to as State of Health (SoH), monitored by the Battery Management System (BMS). The SoH is related to a number of factors monitored and estimated, such as voltage, capacity, resistance, self-discharge, ability to recharge, number of cycles passed etc. However, many of the SoH estimation procedures used today are based on highly simplistic and empirical models of the battery performance, neglecting the very fundamental electrochemical processes. Finite element methodology (FEM) has been a valuable approach for modelling batteries with a range of cell types and geometries. A FEM approach allows for a model based on physical properties of the system - such as thermodynamics, kinetics and diffusion - as opposed to simplified circuit elements representing these processes. However, this approach has so far not been widely applied to exploring factors affecting SoH in LiBs. . Within the current project, we aim to develop new electroanalytical tools which can be used for LiBs diagnostics in situ and operando, and correlate with FEM electrochemical cell simulations, further explore a novel combined electroanalytical experimental-modelling technique, applied to commercial LiB chemistries specifically relevant for grid storage.

Summary of work

We successfully explored the possibilities of correlating the impedance of lab-scale half-cells with controlled chemistry to their SoC, using FEM modelling and direct comparisons of the simulated EIS results with experimental analogues, also including electrode morphological effects. We manage to establish a P2D (pseudo two dimensional) model interpreting capacity fade scenario caused by SEI formation and Li plating in graphite/NMC cells. Based on this model, we can capture battery aging behavior under different operation temperatures and C-rates appropriately. As a further step, we manage to implement ICI (Intermittent current interruption) by using this established P2D multiphysics model for a homemade NMC/graphite button cell, bench-mark to the experimental results.

In the following step, we plan to validate the electrochemical model by comparison the cell resistances obtained from modelled ICI and EIS measurements, and exploit this combined electroanalytical experimental-modelling diagnostic technique for large-scale commercial cell types specifically relevant for grid storage.

Event log

Project reference-group

Prof. Kristina Edström,  UU
Dr. Stefan Thorburn,  ABB
Dr. Anna M. Andersson,  ABB
Prof. Daniel Brandell,  UU

Publications by this researcher

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A surrogate-assisted uncertainty quantification and sensitivity analysis on a coupled electrochemical-thermal battery aging model
Mohammad Alipour,   Litao Yin,   Shiva Sander Tavallaey,   Anna Mikaela Andersson,   Daniel Brandell.
2023,   Journal of Power Sources, vol. 579

An Integrated Flow-Electric-Thermal Model for a Cylindrical Li-Ion Battery Module with a Direct Liquid Cooling Strategy
Litao Yin,   Zeyang Geng,   Are Bjorneklett,   Elisabeth Soderlund,   Torbjorn Thiringer,   Daniel Brandell.
2022,   ENERGY TECHNOLOGY, vol. 10(8)

Implementing intermittent current interruption into Li-ion cell modelling for improved battery diagnostics
Litao Yin,   Zeyang Geng,   Yu-Chuan Chien,   Torbjörn Thiringer,   Matthew J. Lacey,   Anna M. Andersson,   Daniel Brandell.
2022,   Electrochimica Acta, vol. 427

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 2024-01-10 15:22.

Page started: 2019-06-01
Last generated: 2024-01-10