Next Article in Journal
Model-Based Analysis and Optimization of Acidic Tin–Iron Flow Batteries
Previous Article in Journal
Hybrid Ionically Covalently Cross-Linked Network Binder for High-Performance Silicon Anodes in Lithium-Ion Batteries
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Batteries
Volume 9
Issue 5
10.3390/batteries9050277
batteries-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Battery Performance, Ageing, Reliability and Safety

by
Pascal Venet
1,2
1
Ampère, UMR5005, Univ Lyon, Université Claude Bernard Lyon 1, INSA Lyon, Ecole Centrale de Lyon, CNRS, 69622 Villeurbanne, France
2
Eco7/Ampère Joint Research Team for Energy Management and Storage for Transport, 69500 Bron, France
Batteries 2023, 9(5), 277; https://doi.org/10.3390/batteries9050277
Submission received: 11 January 2023 / Accepted: 26 April 2023 / Published: 18 May 2023
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
Versions Notes

1. Context

The development of portable equipment, electric or electrified vehicles and renewable energy is associated with the development of efficient Energy Storage Systems (ESS), such as batteries or supercapacitors. One of the problems with the use of these electrochemical systems is their limited lifespan associated with unavoidable ageing. This ageing affects their performance, cost of ownership and environmental impacts and may affect their safety.
To avoid these complications, it is, therefore, necessary to study the means to improve the dependability (reliability, maintainability, availability and safety) of these ESS.

2. Characterisations, Studies and Analysis of Ageing and Failures

For this purpose, the first step is to study and analyse the failure or ageing mechanisms and modes [1,2,3,4,5,6]. To do this, either the existing data must be exploited, or data must be created by performing accelerated ageing (cycling and/or calendar) of cells [7,8]. This requires the characterisation of each cell in the pack [9]. The study of ageing mechanisms can be conducted through electrical measurements, thermal measurements, chemical measurements, physical measurements or through post-mortem analyses [10,11,12,13,14]. Modelling of the ageing process, taking into account the temperature, state of charge, depth of discharge, C rate, etc., can then be performed [15,16]. When a large amount of data is available, a statistical study or the use of artificial intelligence is also possible [17,18,19].

3. Exploitation of the Results to Improve the Dependability

This knowledge of ageing and failures can be used to optimise the design or maintenance of ESS. This can be done at the cell level or the pack level, considering the dispersions between the cells [20].
It can also allow us to carry out a diagnosis by the intermediary of measurements and adequate methods to ensure the detection of ESS failures or ageing [21,22]. Knowing the level of ageing allows us to estimate the ESS state of health and, in the best case, the remaining useful life can be estimated [17]. The state of health estimation is a function of the BMS (Battery Management System) [18,19]. Considering this state of the BMS can also allow optimal electrical management between cells (during balancing) or thermal management [23].
In multi-source systems, the health status of each energy storage system can also be considered for optimal power management.
The study of different battery charges (especially fast charges) is also important because these can affect their ageing and lifetime [24].
Considering the ageing process is also essential in technical–economic or environmental analyses. For example, the use of second-life batteries can be studied with these criteria [25].
Finally, given the risk of ignition or explosion in the event of a defect or misuse, improving the operational safety of these components is essential [26,27,28]. To investigate this, abuse tests can be performed and/or accidents can be analysed. This can lead to the design of safe cells and packs by integrating dedicated sensors [29] or by integrating specific functions in the BMS.
Numerous studies are, therefore, to be carried out and presented, especially as these depend completely on the battery chemistries.
Many challenges remain to be met to improve the reliability, availability and safety of ESS. Addressing these challenges will make the widespread use of ESS possible.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sgroi, M.F. Lithium-Ion Batteries Aging Mechanisms. Batteries 2022, 8, 205. [Google Scholar] [CrossRef]
  2. Venet, P.; Redondo-Iglesias, E. Batteries and Supercapacitors Aging. Batteries 2020, 6, 18. [Google Scholar] [CrossRef]
  3. Teichert, P.; Eshetu, G.G.; Jahnke, H.; Figgemeier, E. Degradation and Aging Routes of Ni-Rich Cathode Based Li-Ion Batteries. Batteries 2020, 6, 8. [Google Scholar] [CrossRef]
  4. Goldammer, E.; Kowal, J. Determination of the Distribution of Relaxation Times by Means of Pulse Evaluation for Offline and Online Diagnosis of Lithium-Ion Batteries. Batteries 2021, 7, 36. [Google Scholar] [CrossRef]
  5. Frankenberger, M.; Trunk, M.; Seidlmayer, S.; Dinter, A.; Dittloff, J.; Werner, L.; Gernhäuser, R.; Revay, Z.; Märkisch, B.; Gilles, R.; et al. SEI Growth Impacts of Lamination, Formation and Cycling in Lithium-Ion Batteries. Batteries 2020, 6, 21. [Google Scholar] [CrossRef]
  6. Möller, S.; Satoh, T.; Ishii, Y.; Teßmer, B.; Guerdelli, R.; Kamiya, T.; Fujita, K.; Suzuki, K.; Kato, Y.; Wiemhöfer, H.-D.; et al. Absolute Local Quantification of Li as Function of State-of-Charge in All-Solid-State Li Batteries via 2D MeV Ion-Beam Analysis. Batteries 2021, 7, 41. [Google Scholar] [CrossRef]
  7. Werner, D.; Paarmann, S.; Wiebelt, A.; Wetzel, T. Inhomogeneous Temperature Distribution Affecting the Cyclic Aging of Li-Ion Cells. Part I: Experimental Investigation. Batteries 2020, 6, 13. [Google Scholar] [CrossRef]
  8. Werner, D.; Paarmann, S.; Wetzel, T. Calendar Aging of Li-Ion Cells—Experimental Investigation and Empirical Correlation. Batteries 2021, 7, 28. [Google Scholar] [CrossRef]
  9. Gewald, T.; Candussio, A.; Wildfeuer, L.; Lehmkuhl, D.; Hahn, A.; Lienkamp, M. Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging. Batteries 2020, 6, 6. [Google Scholar] [CrossRef]
  10. Werner, D.; Paarmann, S.; Wiebelt, A.; Wetzel, T. Inhomogeneous Temperature Distribution Affecting the Cyclic Aging of Li-Ion Cells. Part II: Analysis and Correlation. Batteries 2020, 6, 12. [Google Scholar] [CrossRef]
  11. Krupp, A.; Ferg, E.; Schuldt, F.; Derendorf, K.; Agert, C. Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells. Batteries 2021, 7, 2. [Google Scholar] [CrossRef]
  12. Mohsin, I.U.; Ziebert, C.; Rohde, M.; Seifert, H.J. Thermophysical Characterization of a Layered P2 Type Structure Na0.53MnO2 Cathode Material for Sodium Ion Batteries. Batteries 2021, 7, 16. [Google Scholar] [CrossRef]
  13. Kuntz, P.; Raccurt, O.; Azaïs, P.; Richter, K.; Waldmann, T.; Wohlfahrt-Mehrens, M.; Bardet, M.; Buzlukov, A.; Genies, S. Identification of Degradation Mechanisms by Post-Mortem Analysis for High Power and High Energy Commercial Li-Ion Cells after Electric Vehicle Aging. Batteries 2021, 7, 48. [Google Scholar] [CrossRef]
  14. Li, Y.; Guo, J.; Pedersen, K.; Gurevich, L.; Stroe, D.-I. Recent Health Diagnosis Methods for Lithium-Ion Batteries. Batteries 2022, 8, 72. [Google Scholar] [CrossRef]
  15. Redondo-Iglesias, E.; Venet, P.; Pelissier, S. Modelling Lithium-Ion Battery Ageing in Electric Vehicle Applications—Calendar and Cycling Ageing Combination Effects. Batteries 2020, 6, 14. [Google Scholar] [CrossRef]
  16. Al-Gabalawy, M.; Hosny, N.S.; Hussien, S.A. Lithium-Ion Battery Modeling Including Degradation Based on Single-Particle Approximations. Batteries 2020, 6, 37. [Google Scholar] [CrossRef]
  17. Lin, H.; Kang, L.; Xie, D.; Linghu, J.; Li, J. Online State-of-Health Estimation of Lithium-Ion Battery Based on Incremental Capacity Curve and BP Neural Network. Batteries 2022, 8, 29. [Google Scholar] [CrossRef]
  18. Falai, A.; Giuliacci, T.A.; Misul, D.A.; Anselma, P.G. Reducing the Computational Cost for Artificial Intelligence-Based Battery State-of-Health Estimation in Charging Events. Batteries 2022, 8, 209. [Google Scholar] [CrossRef]
  19. Zhao, J.; Burke, A.F. Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis. Batteries 2022, 8, 142. [Google Scholar] [CrossRef]
  20. Nenadic, N.G.; Trabold, T.A.; Thurston, M.G. Cell Replacement Strategies for Lithium Ion Battery Packs. Batteries 2020, 6, 39. [Google Scholar] [CrossRef]
  21. Manfredini, G.; Ria, A.; Bruschi, P.; Gerevini, L.; Vitelli, M.; Molinara, M.; Piotto, M. An ASIC-Based Miniaturized System for Online Multi-Measurand Monitoring of Lithium-Ion Batteries. Batteries 2021, 7, 45. [Google Scholar] [CrossRef]
  22. Riviere, E.; Sari, A.; Venet, P.; Meniere, F.; Bultel, Y. Innovative Incremental Capacity Analysis Implementation for C/LiFePO4 Cell State-of-Health Estimation in Electrical Vehicles. Batteries 2019, 5, 37. [Google Scholar] [CrossRef]
  23. Li, A.; Yuen, A.C.Y.; Wang, W.; Chen, T.B.Y.; Lai, C.S.; Yang, W.; Wu, W.; Chan, Q.N.; Kook, S.; Yeoh, G.H. Integration of Computational Fluid Dynamics and Artificial Neural Network for Optimization Design of Battery Thermal Management System. Batteries 2022, 8, 69. [Google Scholar] [CrossRef]
  24. Dotoli, M.; Milo, E.; Giuliano, M.; Rocca, R.; Nervi, C.; Baricco, M.; Ercole, M.; Sgroi, M.F. Detection of Lithium Plating in Li-Ion Cell Anodes Using Realistic Automotive Fast-Charge Profiles. Batteries 2021, 7, 46. [Google Scholar] [CrossRef]
  25. Montes, T.; Etxandi-Santolaya, M.; Eichman, J.; Ferreira, V.J.; Trilla, L.; Corchero, C. Procedure for Assessing the Suitability of Battery Second Life Applications after EV First Life. Batteries 2022, 8, 122. [Google Scholar] [CrossRef]
  26. Kovachev, G.; Astner, A.; Gstrein, G.; Aiello, L.; Hemmer, J.; Sinz, W.; Ellersdorfer, C. Thermal Conductivity in Aged Li-Ion Cells under Various Compression Conditions and State-of-Charge. Batteries 2021, 7, 42. [Google Scholar] [CrossRef]
  27. Aiello, L.; Hanzu, I.; Gstrein, G.; Ewert, E.; Ellersdorfer, C.; Sinz, W. Analysis and Investigation of Thermal Runaway Propagation for a Mechanically Constrained Lithium-Ion Pouch Cell Module. Batteries 2021, 7, 49. [Google Scholar] [CrossRef]
  28. Darnikowski, D.; Mieloszyk, M. Investigation into the Lithium-Ion Battery Fire Resistance Testing Procedure for Commercial Use. Batteries 2021, 7, 44. [Google Scholar] [CrossRef]
  29. Essl, C.; Seifert, L.; Rabe, M.; Fuchs, A. Early Detection of Failing Automotive Batteries Using Gas Sensors. Batteries 2021, 7, 25. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Venet, P. Battery Performance, Ageing, Reliability and Safety. Batteries 2023, 9, 277. https://doi.org/10.3390/batteries9050277

AMA Style

Venet P. Battery Performance, Ageing, Reliability and Safety. Batteries. 2023; 9(5):277. https://doi.org/10.3390/batteries9050277

Chicago/Turabian Style

Venet, Pascal. 2023. "Battery Performance, Ageing, Reliability and Safety" Batteries 9, no. 5: 277. https://doi.org/10.3390/batteries9050277

APA Style

Venet, P. (2023). Battery Performance, Ageing, Reliability and Safety. Batteries, 9(5), 277. https://doi.org/10.3390/batteries9050277

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop