A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis

被引:19
作者
Chacon, Ximena Carolina Acaro [1 ]
Laureti, Stefano [1 ]
Ricci, Marco [1 ]
Cappuccino, Gregorio [1 ]
机构
[1] Univ Calabria, Dept Informat Modelling Elect & Syst Engn, I-87036 Arcavacata Di Rende, Italy
来源
WORLD ELECTRIC VEHICLE JOURNAL | 2023年 / 14卷 / 11期
关键词
non-destructive testing; non-destructive evaluation; lithium battery; experimental setup; state-of-health; second life usage; electrochemical impedance spectroscopy; infrared thermography; X-ray computed tomography; ultrasonic testing; electric vehicles; safety concerns; TIME-OF-FLIGHT; THERMAL RUNAWAY; POLYMER BATTERIES; QUALITY-CONTROL; STATE; HEALTH; CHARGE; THERMOGRAPHY; SPECTROSCOPY; OPERANDO;
D O I
10.3390/wevj14110305
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of health accurately, as well as estimating a range of relevant parameters in order to ensure their safe and efficient use. In this framework, non-destructive inspection methods play a fundamental role in assessing the condition of lithium-ion batteries, allowing for their thorough examination without causing any damage. This aspect is particularly crucial when batteries are exploited in critical applications and when evaluating the potential second life usage of the cells. This review explores various non-destructive methods for evaluating lithium batteries, i.e., electrochemical impedance spectroscopy, infrared thermography, X-ray computed tomography and ultrasonic testing, considers and compares several aspects such as sensitivity, flexibility, accuracy, complexity, industrial applicability, and cost. Hence, this work aims at providing academic and industrial professionals with a tool for choosing the most appropriate methodology for a given application.
引用
收藏
页数:22
相关论文
共 114 条
[1]   A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries [J].
Akbar, Khalid ;
Zou, Yuan ;
Awais, Qasim ;
Baig, Mirza Jabbar Aziz ;
Jamil, Mohsin .
ELECTRONICS, 2022, 11 (08)
[2]   Recent Industrial Applications of Infrared Thermography: A Review [J].
Alfredo Osornio-Rios, Roque ;
Alfonso Antonino-Daviu, Jose ;
de Jesus Romero-Troncoso, Rene .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 15 (02) :615-625
[3]   Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling [J].
Andre, D. ;
Meiler, M. ;
Steiner, K. ;
Walz, H. ;
Soczka-Guth, T. ;
Sauer, D. U. .
JOURNAL OF POWER SOURCES, 2011, 196 (12) :5349-5356
[4]   An Overview of Non-Destructive Testing Methods for Integrated Circuit Packaging Inspection [J].
Aryan, Pouria ;
Sampath, Santhakumar ;
Sohn, Hoon .
SENSORS, 2018, 18 (07)
[5]   Energy and environmental aspects in recycling lithium-ion batteries: Concept of Battery Identity Global Passport [J].
Bai, Yaocai ;
Muralidharan, Nitin ;
Sun, Yang-Kook ;
Passerini, Stefano ;
Whittingham, M. Stanley ;
Belharouak, Ilias .
MATERIALS TODAY, 2020, 41 :304-315
[6]   A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations [J].
Balakrishnan, Ganesh Kumar ;
Yaw, Chong Tak ;
Koh, Siaw Paw ;
Abedin, Tarek ;
Raj, Avinash Ashwin ;
Tiong, Sieh Kiong ;
Chen, Chai Phing .
ENERGIES, 2022, 15 (16)
[7]   Recent Advances on Materials for Lithium-Ion Batteries [J].
Barbosa, Joao C. ;
Goncalves, Renato ;
Costa, Carlos M. ;
Lanceros-Mendez, Senentxu .
ENERGIES, 2021, 14 (11)
[8]   Predicting heat generation in a lithium-ion pouch cell through thermography and the lumped capacitance model [J].
Bazinski, S. J. ;
Wang, X. .
JOURNAL OF POWER SOURCES, 2016, 305 :97-105
[9]  
Buyukozturk O., 2013, Nondestructive testing of materials and structures: proceedings of NDTMS-2011
[10]   Lithium-Ion Battery State of Health Estimation Based on Electrochemical Impedance Spectroscopy and Cuckoo Search Algorithm Optimized Elman Neural Network [J].
Chang, Chun ;
Wang, Shaojin ;
Jiang, Jiuchun ;
Gao, Yang ;
Jiang, Yan ;
Liao, Li .
JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE, 2022, 19 (03)