Publication:
Applied method to model the thermal runaway of lithium-ion batteries

Consultable a partir de

2022-11-03

Date

2021

Director

Publisher

IEEE
Acceso abierto / Sarbide irekia
Contribución a congreso / Biltzarrerako ekarpena
Versión aceptada / Onetsi den bertsioa

Project identifier

AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111262RB-I00/ES/
European Commission/Horizon 2020 Framework Programme/774094openaire

Abstract

The thermal runaway (TR) is one of the most dangerous phenomena related to lithium-ion batteries. For this reason, there are different proposals in the literature for its modelling. Most of these proposed models take into account the decomposition reactions between the internal components of the cell, and base the adjustment of the parameters on numerous abuse tests that lead to the appearance of TR. However, these tests are destructive, require specific equipment, present a high economic cost and are very time consuming. This paper proposes a modelling method which enables the development of TR models with the use of fewer resources. This method is based on chemical kinetics, which allow a simplification of the general modelling process published in the literature. At the same time it maintains good accuracy and makes it possible to define the TR behavior of any type of cell, regardless of its chemistry, shape or size. Furthermore, the proposed method allows the use of the experimental results most commonly presented in the specialized literature, which significantly reduces the need for destructive testing. The presented modelling method achieves a good compromise between accuracy and applicability in the validations shown in the paper.

Keywords

Thermal runaway, Lithium-ion batteries, Modelling, Chemical kinetics

Department

Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren / Institute of Smart Cities - ISC / Ingeniería Eléctrica, Electrónica y de Comunicación

Faculty/School

Degree

Doctorate program

Editor version

Funding entities

This work has been supported by the Spanish State Research Agency (AEI) under grant PID2019-111262RB-I00 /AEI/ 10.13039/501100011033, the European Union under the H2020 project STARDUST (774094), and the Public University of Navarra under project ReBMS PJUPNA1904.

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