journal article·2019Open access

Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries

Jorn M. Reniers, Grietus Mulder, David A. Howey

Abstract

The maximum energy that lithium-ion batteries can store decreases as they are used because of various irreversible degradation mechanisms. Many models of degradation have been proposed in the literature, sometimes with a small experimental data set for validation. However, a comprehensive comparison between different model predictions is lacking, making it difficult to select modelling approaches which can explain the degradation trends actually observed from data. Here, various degradation models from literature are implemented within a single particle model framework and their behavior is compared. It is shown that many different models can be fitted to a small experimental data set. The interactions between different models are simulated, showing how some of the models accelerate degradation in other models, altering the overall degradation trend. The effects of operating conditions on the various degradation models is simulated. This identifies which models are enhanced by which operating conditions and might therefore explain specific degradation trends observed in data. Finally, it is shown how a combination of different models is needed to capture different degradation trends observed in a large experimental data set. Vice versa, only a large data set enables to properly select the models which best explain the observed degradation.
Source: openalex_inverted_index · Quality: baseline

Research context

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

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

References (92)

Show source-provided references
1. 10.1016/j.apenergy.2017.05.01810.1016/j.apenergy.2017.05.018 ↗
2. 10.1016/j.apenergy.2016.03.11910.1016/j.apenergy.2016.03.119 ↗
3. 10.1016/j.jpowsour.2018.01.00410.1016/j.jpowsour.2018.01.004 ↗
4. 10.1149/2.1181714jes10.1149/2.1181714jes ↗
5. 10.23919/ACC.2017.796357810.23919/ACC.2017.7963578 ↗
6. 10.1016/j.jpowsour.2013.05.04010.1016/j.jpowsour.2013.05.040 ↗
7. 10.1016/j.jpowsour.2016.07.03610.1016/j.jpowsour.2016.07.036 ↗
8. 10.1016/j.jpowsour.2007.08.05710.1016/j.jpowsour.2007.08.057 ↗
9. 10.1088/1755-1315/93/1/01204010.1088/1755-1315/93/1/012040 ↗
10. 10.1109/MIM.2008.457926910.1109/MIM.2008.4579269 ↗
11. 10.1109/TIA.2014.236515210.1109/TIA.2014.2365152 ↗
12. Snoek J. Larochelle H. Adams R. P. , Practical Bayesian Optimization of Machine Learning Algorithms, in: Pereira F. Burges C. J. C. Bottou L. Weinberger K. Q. (Eds.), Advances in Neural Information Processing Systems 25, Curran Associates, Inc., pp. 2951–2959 (2012).
13. 10.1016/j.jpowsour.2012.11.14610.1016/j.jpowsour.2012.11.146 ↗
14. 10.1016/j.jpowsour.2011.03.10110.1016/j.jpowsour.2011.03.101 ↗
15. Howey D. A. Reniers J. M. , SLIDE (2019). https://github.com/davidhowey/SLIDE.
16. 10.1149/2.1061613jes10.1149/2.1061613jes ↗
17. 10.1016/j.jpowsour.2016.08.05410.1016/j.jpowsour.2016.08.054 ↗
18. 10.1149/2.0241509jes10.1149/2.0241509jes ↗
19. 10.1149/2.1051507jes10.1149/2.1051507jes ↗
20. 10.1149/1.187075210.1149/1.1870752 ↗
21. 10.1149/2.049208jes10.1149/2.049208jes ↗
22. 10.1149/2.049210jes10.1149/2.049210jes ↗
23. 10.1149/2.0841702jes10.1149/2.0841702jes ↗
24. 10.1149/2.0641506jes10.1149/2.0641506jes ↗
25. 10.1149/2.0461706jes10.1149/2.0461706jes ↗
26. 10.1016/j.jpowsour.2016.12.09910.1016/j.jpowsour.2016.12.099 ↗
27. 10.1149/2.1101902jes10.1149/2.1101902jes ↗
28. 10.1149/2.0321712jes10.1149/2.0321712jes ↗
29. 10.1149/2.0761702jes10.1149/2.0761702jes ↗
30. 10.1149/2.0941814jes10.1149/2.0941814jes ↗
31. 10.1016/j.jpowsour.2015.09.03310.1016/j.jpowsour.2015.09.033 ↗
32. 10.1016/j.jpowsour.2013.06.13010.1016/j.jpowsour.2013.06.130 ↗
33. 10.1149/2.0161506jes10.1149/2.0161506jes ↗
34. 10.1149/2.040310jes10.1149/2.040310jes ↗
35. 10.1149/2.013302jes10.1149/2.013302jes ↗
36. 10.1149/1.178763110.1149/1.1787631 ↗
37. 10.1149/2.044302jes10.1149/2.044302jes ↗
38. 10.1149/1.164460110.1149/1.1644601 ↗
39. 10.1016/j.jpowsour.2014.07.02810.1016/j.jpowsour.2014.07.028 ↗
40. 10.1149/1.163427310.1149/1.1634273 ↗
41. 10.1016/j.jpowsour.2012.02.11410.1016/j.jpowsour.2012.02.114 ↗
42. 10.1149/1.304342910.1149/1.3043429 ↗
43. 10.1149/1.343256010.1149/1.3432560 ↗
44. 10.1149/2.103112jes10.1149/2.103112jes ↗
45. 10.1149/2.0121711jes10.1149/2.0121711jes ↗
46. 10.1149/2.1581706jes10.1149/2.1581706jes ↗
47. 10.1149/2.025211jes10.1149/2.025211jes ↗
48. 10.1016/j.jpowsour.2017.05.11010.1016/j.jpowsour.2017.05.110 ↗
49. 10.1021/jz100188d10.1021/jz100188d ↗
50. 10.1021/jp303610t10.1021/jp303610t ↗
51. 10.1021/nl202764f10.1021/nl202764f ↗
52. 10.1002/cite.20180018610.1002/cite.201800186 ↗
53. 10.1149/1.205468410.1149/1.2054684 ↗
54. 10.1021/jp311431z10.1021/jp311431z ↗
55. 10.1016/j.jpowsour.2016.12.08310.1016/j.jpowsour.2016.12.083 ↗
56. 10.1016/j.est.2016.01.00310.1016/j.est.2016.01.003 ↗
57. 10.1149/1.212926910.1149/1.2129269 ↗
58. 10.1149/1.183891810.1149/1.1838918 ↗
59. 10.1016/S0378-7753(98)00092-510.1016/S0378-7753(98)00092-5 ↗
60. 10.1149/1.352131410.1149/1.3521314 ↗
61. 10.1016/j.electacta.2010.05.07210.1016/j.electacta.2010.05.072 ↗
62. 10.1016/j.jpowsour.2014.06.02410.1016/j.jpowsour.2014.06.024 ↗
63. 10.1021/acs.jpclett.5b0172710.1021/acs.jpclett.5b01727 ↗
64. 10.1021/jp411101910.1021/jp4111019 ↗
65. 10.1039/C7CC07082A10.1039/C7CC07082A ↗
66. 10.1016/j.coelec.2018.10.01310.1016/j.coelec.2018.10.013 ↗
67. 10.1002/cssc.20180007710.1002/cssc.201800077 ↗
68. 10.1149/2.0241904jes10.1149/2.0241904jes ↗
69. Basquin, The exponential law of endurance tests, in: Proc. ASTM 10, 1910, pp. 625–630 (1910).
70. 10.1016/j.jpowsour.2013.08.11310.1016/j.jpowsour.2013.08.113 ↗
71. 10.1016/j.jpowsour.2017.05.11510.1016/j.jpowsour.2017.05.115 ↗
72. 10.1149/2.011310jes10.1149/2.011310jes ↗
73. 10.1149/2.1541704jes10.1149/2.1541704jes ↗
74. 10.1016/j.jpowsour.2012.09.09610.1016/j.jpowsour.2012.09.096 ↗
75. 10.1016/j.jpowsour.2019.02.07310.1016/j.jpowsour.2019.02.073 ↗
76. 10.1149/2.0611809jes10.1149/2.0611809jes ↗
77. 10.1016/j.jpowsour.2005.01.00610.1016/j.jpowsour.2005.01.006 ↗
78. 10.1016/j.est.2015.05.00310.1016/j.est.2015.05.003 ↗
79. 10.1149/2.0461810jes10.1149/2.0461810jes ↗
80. 10.1039/C6RA19482F10.1039/C6RA19482F ↗
81. 10.1149/2.0291510jes10.1149/2.0291510jes ↗
82. 10.1016/j.jpowsour.2015.07.01910.1016/j.jpowsour.2015.07.019 ↗
83. 10.1016/j.trb.2017.01.02010.1016/j.trb.2017.01.020 ↗
84. European Commission, EVERLASTING (2018). https://ec.europa.eu/inea/en/horizon-2020/projects/h2020-transport/green-vehicles/everlasting.
85. LG Chem, Rechargeable Lithium Ion Battery Model : INR18650 MJ1 3500mAh, PRODUCT SPECIFICATION (2014). https://www.nkon.nl/sk/k/SpecificationINR18650MJ122.08.2014.pdf.
86. European Commission, MAT4BAT (2016). http://cordis.europa.eu/project/rcn/109052_en.html.
87. Kokam, KOKAM Li-ion/Polymer Cell (2016). http://kokam.com/wp-content/uploads/2016/03/SLPB-Cell-Brochure.pdf.
88. 10.1016/j.jpowsour.2017.05.01510.1016/j.jpowsour.2017.05.015 ↗
89. 10.1016/j.jpowsour.2014.02.01210.1016/j.jpowsour.2014.02.012 ↗
90. 10.1016/j.apenergy.2015.04.11010.1016/j.apenergy.2015.04.110 ↗
91. 10.1149/2.0401707jes10.1149/2.0401707jes ↗
92. 10.1016/j.jpowsour.2014.01.10510.1016/j.jpowsour.2014.01.105 ↗