State of Health analysis for Lithium-ion Batteries considering temperature effect

被引：4
|
作者：
Lashgari, F. [1 ]
Petkovski, E. [1 ]
Cristaldi, L. [1 ]
机构：
[1] Politecn Milan, DEIB, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy
来源：
2022 IEEE INTERNATIONAL CONFERENCE ON METROLOGY FOR EXTENDED REALITY, ARTIFICIAL INTELLIGENCE AND NEURAL ENGINEERING (METROXRAINE) | 2022年
关键词：
MANAGEMENT; PROGNOSTICS; PREDICTION; SYSTEMS;
D O I：
10.1109/MetroXRAINE54828.2022.9967550
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Lithium-ion batteries have become an integral component of machines and products in every field of modern life. In order to assure optimal use of the batteries, it is necessary to accurately predict their various parameters such as state-of-health (SoH), end of life (EoL) and state-of-charge (SoC). In this paper the use of the third-degree polynomial and hybrid function for SoH estimation and remaining useful life (RUL) prediction are further validated on a different dataset. Furthermore, linear interpolation is used to enlarge the dataset and achieve more accurate results. Finally, the battery state-of-health estimation in terms of temperature dependency is analyzed.
引用
收藏
页码：40 / 45
页数：6
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