journal article·2017Open access

Comparing Macroscale and Microscale Simulations of Porous Battery Electrodes

Kenneth Higa, Shao-Ling Wu, Dilworth Y. Parkinson, Yanbao Fu, Steven Ferreira, Vincent Battaglia, Venkat Srinivasan

Abstract

This describes a vertically-integrated exploration of NMC electrode rate limitations, combining experiments with corresponding macroscale (macro-homogeneous) and microscale models. Parameters common to both models were obtained from experiments or based on published results. Positive electrode tortuosity was the sole fitting parameter used in the macroscale model, while the microscale model used no fitting parameters, instead relying on microstructural domains generated from X-ray microtomography of pristine electrode material held under compression while immersed in electrolyte solution (additionally providing novel observations of electrode wetting). Macroscale simulations showed that the capacity decrease observed at higher rates resulted primarily from solution-phase diffusion resistance. This ability to provide such qualitative insights at low computational costs is a strength of macroscale models, made possible by neglecting electrode spatial details. To explore the consequences of such simplification, the corresponding, computationally-expensive microscale model was constructed. This was found to have limitations preventing quantitatively accurate predictions, for reasons that are discussed in the hope of guiding future work. Nevertheless, the microscale simulation results complement those of the macroscale model by providing a reality-check based on microstructural information; in particular, this novel comparison of the two approaches suggests a reexamination of salt diffusivity measurements.
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 (56)

Show source-provided references
1. Newman J. S. Thomas-Alyea K. E. , Electrochemical Systems, John Wiley & Sons, Inc. (2004).
2. 10.1149/1.222159710.1149/1.2221597 ↗
3. 10.1002/aic.69021010310.1002/aic.690210103 ↗
4. 10.1149/1.357999610.1149/1.3579996 ↗
5. 10.1016/j.jpowsour.2010.04.06610.1016/j.jpowsour.2010.04.066 ↗
6. 10.1149/2.002203esl10.1149/2.002203esl ↗
7. 10.1149/2.024210jes10.1149/2.024210jes ↗
8. 10.1149/1.321600010.1149/1.3216000 ↗
9. 10.1002/aenm.20120093210.1002/aenm.201200932 ↗
10. 10.1149/2.096205jes10.1149/2.096205jes ↗
11. 10.1149/2.024210jes10.1149/2.024210jes ↗
12. 10.1016/j.electacta.2012.10.10410.1016/j.electacta.2012.10.104 ↗
13. 10.1016/j.electacta.2013.10.10310.1016/j.electacta.2013.10.103 ↗
14. 10.1016/j.electacta.2015.12.22410.1016/j.electacta.2015.12.224 ↗
15. 10.1016/S0378-7753(99)00221-910.1016/S0378-7753(99)00221-9 ↗
16. 10.1016/S0378-7753(00)00407-910.1016/S0378-7753(00)00407-9 ↗
17. 10.1246/cl.2001.64210.1246/cl.2001.642 ↗
18. 10.1149/1.150563310.1149/1.1505633 ↗
19. 10.1016/S0013-4686(02)00593-510.1016/S0013-4686(02)00593-5 ↗
20. 10.1016/S0378-7753(03)00173-310.1016/S0378-7753(03)00173-3 ↗
21. 10.1016/j.jpowsour.2004.10.01610.1016/j.jpowsour.2004.10.016 ↗
22. 10.1021/cm048433e10.1021/cm048433e ↗
23. 10.1149/1.195492710.1149/1.1954927 ↗
24. 10.1016/j.jpowsour.2008.12.03610.1016/j.jpowsour.2008.12.036 ↗
25. 10.1016/S0378-7753(03)00529-910.1016/S0378-7753(03)00529-9 ↗
26. 10.1016/j.jpowsour.2005.03.08010.1016/j.jpowsour.2005.03.080 ↗
27. 10.1016/j.electacta.2003.09.00910.1016/j.electacta.2003.09.009 ↗
28. 10.1149/2.062204jes10.1149/2.062204jes ↗
29. 10.1149/1.184768510.1149/1.1847685 ↗
30. 10.1038/nmeth.201910.1038/nmeth.2019 ↗
31. 10.1038/nmeth.208910.1038/nmeth.2089 ↗
32. 10.1115/1.403441010.1115/1.4034410 ↗
33. 10.1149/1.205468410.1149/1.2054684 ↗
34. 10.1007/b11378810.1007/b113788 ↗
35. 10.1149/1.278377210.1149/1.2783772 ↗
36. 10.1021/cr030203g10.1021/cr030203g ↗
37. 10.1149/1.280137810.1149/1.2801378 ↗
38. Stewart S. G. , Ph.D. dissertation, University of California, Berkeley (2007).
39. 10.1149/1.187273710.1149/1.1872737 ↗
40. 10.1149/2.0271603jes10.1149/2.0271603jes ↗
41. 10.1149/2.0341609jes10.1149/2.0341609jes ↗
42. 10.1149/2.079112jes10.1149/2.079112jes ↗
43. 10.1149/2.jes11322410.1149/2.jes113224 ↗
44. 10.1149/2.0641503jes10.1149/2.0641503jes ↗
45. Mehrotra A. Srinivasan V. , The 224th Electrochemical Society Meeting, Abstract 1152 (2013).
46. 10.1002/andp.1935416070510.1002/andp.19354160705 ↗
47. 10.1016/j.jpowsour.2008.12.03210.1016/j.jpowsour.2008.12.032 ↗
48. 10.1016/j.elecom.2012.12.02110.1016/j.elecom.2012.12.021 ↗
49. 10.1016/j.jpowsour.2015.02.08610.1016/j.jpowsour.2015.02.086 ↗
50. 10.1017/S143192761501564010.1017/S1431927615015640 ↗
51. 10.1016/j.elecom.2012.11.00610.1016/j.elecom.2012.11.006 ↗
52. 10.1149/2.033207jes10.1149/2.033207jes ↗
53. Jones E. Oliphant E. Peterson P. , Scipy: Open source scientific tools for Python, http://www.scipy.org/ (2001).
54. Schroeder W. Martin K. Lorensen B. , The Visualization Toolkit (4th ed.), Kitware (2006).
55. Siemens Product Lifecycle Management Software Inc., STAR-CCM+ User Guide, version 11.06.010 (2016).
56. Chiang Y. M. Bae C. J. Halloran J. W. Fu Q. Tomsia A. P. Erdonmez C. K. , in US Patents US20120315538 A1 (2012).