journal article·2025-06-22Open access

Impact of Temperature and Depth of Discharge on Commercial Nickel Manganese Oxide and Lithium Iron Phosphate Batteries After Three Years of Aging

Matthieu Dubarry, Andrew Pearson, Keiran Pringle, Youssof Shekibi, Steven Pas

Batteries· Volume 11· Issue 7

Abstract

Accurate cell selection is primordial to ensure battery safety and longevity. Unfortunately, because of path dependence, finding out which cells are best adapted to a specific application is not straightforward and might require significant testing. This work provides the analysis of three years of aging, both cycling and calendar, for two batches of commercial cells of different chemistries. Using design of experiments and analysis of variance, this work showed that the impact of temperature and depth of discharge, both at the beginning and end of discharge, are chemistry dependent. Moreover, an analysis of the cells’ degradation modes also showcased different pathways depending on the positive electrode chemistry and the type of aging.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Hawaiʻi Natural Energy Institute, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA

Platform Division, Defence Science and Technology Group, Melbourne, VIC 3207, Australia

Funding

DST Group

References (48)

Show source-provided references
1. A Review and Perspective on Path Dependency in Batteries10.1002/ente.202200627 ↗
2. Investigation of Path-Dependent Degradation in Lithium-Ion Batteries10.1002/batt.202000160 ↗
3. Ageing mechanisms in lithium-ion batteries10.1016/j.jpowsour.2005.01.006 ↗
4. Degradation diagnostics for lithium ion cells10.1016/j.jpowsour.2016.12.011 ↗
5. ENPOLITE: Comparing Lithium-Ion Cells across Energy, Power, Lifetime, and Temperature10.1021/acsenergylett.1c00743 ↗
6. Operando lithium plating quantification and early detection of a commercial LiFePO 4 cell cycled under dynamic driving schedule10.1016/j.jpowsour.2017.04.072 ↗
7. Antony, J. (2003). Design of Experiments for Engineers and Scientists, Elsevier Science & Technology Books.
8. Montgomery, D. (2013). Design and Analysis of Experiments, WILEY. [8th ed.].
9. R. A. Fisher and the Design of Experiments, 1922–1926
10. Design-of-Experiment and Statistical Modeling of a Large Scale Aging Experiment for Two Popular Lithium Ion Cell Chemistries10.1149/2.003308jes ↗
11. Search for an Optimal Rapid-Charging Pattern for Li-Ion Batteries Using the Taguchi Approach10.1109/TIE.2009.2036020 ↗
12. Multi-stress factor model for cycle lifetime prediction of lithium ion batteries with shallow-depth discharge10.1016/j.jpowsour.2015.01.003 ↗
13. Zhao, R., Lorenz, R.D., Jahns, T.M., and Juang, L. (2016, January 18–22). Separating key less wellknown properties of drive profiles that affect lithium-ion battery aging by applying the statistical design of experiments. Proceedings of the 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA.10.1109/ECCE.2016.7855426 ↗
14. Closed-loop optimization of fast-charging protocols for batteries with machine learning10.1038/s41586-020-1994-5 ↗
15. Saxena, S., Roman, D., Robu, V., Flynn, D., and Pecht, M. (2021). Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning. Energies, 14.10.3390/en14030723 ↗
16. Battery durability and reliability under electric utility grid operations: Representative usage aging and calendar aging10.1016/j.est.2018.04.004 ↗
17. Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Path Dependence of Battery Degradation10.1149/2.0421805jes ↗
18. Durability and reliability of electric vehicle batteries under electric utility grid operations: Bidirectional charging impact analysis10.1016/j.jpowsour.2017.05.015 ↗
19. Investigation of the influence of superimposed AC current on lithium-ion battery aging using statistical design of experiments10.1016/j.est.2017.02.005 ↗
20. Baghdadi, I., Mathieu, R., Briat, O., Gyan, P., and Vinassa, J.-M. (2017, January 11–14). Lithium-ion battery ageing assessment based on a reduced design of experiments. Proceedings of the 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), Belfort, France.10.1109/VPPC.2017.8330871 ↗
21. Fast charging of lithium-ion cells: Identification of aging-minimal current profiles using a design of experiment approach and a mechanistic degradation analysis10.1016/j.est.2018.08.002 ↗
22. Investigation and modeling of cyclic aging using a design of experiment with automotive grade lithium-ion cells10.1016/j.jpowsour.2021.230952 ↗
23. Identifying main factors of capacity fading in lithium ion cells using orthogonal design of experiments10.1016/j.apenergy.2015.11.014 ↗
24. Performance metrics for the comparison of lithium ion cell aging experiments10.1016/j.enrev.2023.100025 ↗
25. Application of an experimental design approach to optimize aging protocols for lithium-metal batteries10.1016/j.fub.2025.100041 ↗
26. Multi-factor aging in Lithium Iron phosphate batteries: Mechanisms and insights10.1016/j.apenergy.2024.125250 ↗
27. D-Optimal design of experiments applied to lithium battery for ageing model calibration10.1016/j.energy.2017.11.130 ↗
28. Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model10.1149/2.0421807jes ↗
29. Statistical investigation of temperature-dependent cycle lifetime and cell-to-cell variance in lithium-ion batteries: A model-based approach10.1016/j.jpowsour.2024.235334 ↗
30. Exploiting Materials to their Full Potential, a Li-ion Battery Electrode Formulation Optimization Study10.1021/acsaem.0c00015 ↗
31. Rynne, O., Dubarry, M., Molson, C., Lepage, D., Prébé, A., Aymé-Perrot, D., Rochefort, D., and Dollé, M. (2019). Designs of Experiments for Beginners—A Quick Start Guide for Application to Electrode Formulation. Batteries, 5.10.3390/batteries5040072 ↗
32. Román-Ramírez, L.A., and Marco, J. (2022). Design of experiments applied to lithium-ion batteries: A literature review. Appl. Energy, 320.10.1016/j.apenergy.2022.119305 ↗
33. A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells10.1016/j.pecs.2019.01.001 ↗
34. Degradation of Commercial Lithium-ion Cells as a Function of Chemistry and Cycling Conditions10.1149/1945-7111/abae37 ↗
35. Analysis and modeling of cycle aging of a commercial LiFePO4/graphite cell10.1016/j.jpowsour.2019.227666 ↗
36. Depth of discharge characteristics and control strategy to optimize electric vehicle battery life10.1016/j.est.2022.106477 ↗
37. How do Depth of Discharge, C-rate and Calendar Age Affect Capacity Retention, Impedance Growth, the Electrodes, and the Electrolyte in Li-Ion Cells?
38. Calendar aging of commercial Li-ion cells of different chemistries—A review10.1016/j.coelec.2018.05.023 ↗
39. Geisbauer, C., Wöhrl, K., Koch, D., Wilhelm, G., Schneider, G., and Schweiger, H.-G. (2021). Comparative Study on the Calendar Aging Behavior of Six Different Lithium-Ion Cell Chemistries in Terms of Parameter Variation. Energies, 14.10.3390/en14113358 ↗
40. A semi-empirical model of the calendar ageing of lithium-ion batteries aimed at automotive and deep-space applications10.1016/j.est.2023.110388 ↗
41. Sui, X., Świerczyński, M., Teodorescu, R., and Stroe, D.-I. (2021). The Degradation Behavior of LiFePO4/C Batteries during Long-Term Calendar Aging. Energies, 14.10.3390/en14061732 ↗
42. Synthesize battery degradation modes via a diagnostic and prognostic model10.1016/j.jpowsour.2012.07.016 ↗
43. Dubarry, M., and Baure, G. (2020). Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols. Electronics, 9.10.3390/electronics9010152 ↗
44. Investigations on the Effective Electric Loads in Blended Insertion Electrodes for Lithium-Ion Batteries10.1002/celc.201901554 ↗
45. Beck, D., and Dubarry, M. (2024). Electrode Blending Simulations Using the Mechanistic Degradation Modes Modeling Approach. Batteries, 10.10.3390/batteries10050159 ↗
46. Identify capacity fading mechanism in a commercial LiFePO4 cell10.1016/j.jpowsour.2009.05.036 ↗
47. Origins and accommodation of cell variations in Li-ion battery pack modeling10.1002/er.1668 ↗
48. Dubarry, M., and Anseán, D. (2022). Best practices for incremental capacity analysis. Front. Energy Res., 10.10.3389/fenrg.2022.1023555 ↗