Degradation behaviour analysis and end-of-life prediction of lithium titanate oxide batteries

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367002" target="_blank" >RIV/68407700:21230/23:00367002 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.est.2023.107745" target="_blank" >https://doi.org/10.1016/j.est.2023.107745</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.est.2023.107745" target="_blank" >10.1016/j.est.2023.107745</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Degradation behaviour analysis and end-of-life prediction of lithium titanate oxide batteries

Popis výsledku v původním jazyce

Lithium-ion batteries (LiBs) with Lithium titanate oxide Li4Ti5O12(LTO) negative electrodes are an alternative to graphite-based LiBs for high power applications. These cells offer a long lifetime, a wide operating temperature, and improved safety. To ensure the longevity and reliability of the LTO cells in different applications, battery health diagnosis, and lifetime prediction are crucial. This paper examines the cycling ageing behaviour of LTO cells in two different cell temperatures under high-current cycling conditions and various cycle depth (CD) tests. The ageing behaviour is investigated via capacity degradation trend using data-driven technique based on feedforward neural network (FFNN). The model is later validated with the experimental result collected in-house and the lifetime data provided by the manufacturer. The proposed method accurately determines the state of health (SOH) level and predicts the end of life (EOL) with an acceptable error of 5 %.

Název v anglickém jazyce

Degradation behaviour analysis and end-of-life prediction of lithium titanate oxide batteries

Popis výsledku anglicky

Lithium-ion batteries (LiBs) with Lithium titanate oxide Li4Ti5O12(LTO) negative electrodes are an alternative to graphite-based LiBs for high power applications. These cells offer a long lifetime, a wide operating temperature, and improved safety. To ensure the longevity and reliability of the LTO cells in different applications, battery health diagnosis, and lifetime prediction are crucial. This paper examines the cycling ageing behaviour of LTO cells in two different cell temperatures under high-current cycling conditions and various cycle depth (CD) tests. The ageing behaviour is investigated via capacity degradation trend using data-driven technique based on feedforward neural network (FFNN). The model is later validated with the experimental result collected in-house and the lifetime data provided by the manufacturer. The proposed method accurately determines the state of health (SOH) level and predicts the end of life (EOL) with an acceptable error of 5 %.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Energy Storage

ISSN

2352-152X

e-ISSN

2352-1538

Svazek periodika

68

Číslo periodika v rámci svazku

107745

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001015729300001

EID výsledku v databázi Scopus

2-s2.0-85163403611