Integrating Machine Learning and Characterization in Battery Research: Toward Cognitive Digital Twins with Physics and Knowledge
Source abstract
Abstract
Abstract The rapid advancement of battery technology has driven the need for innovative approaches to enhance battery management systems. In response, the concept of a cognitive digital twin has been developed to serve as a sophisticated virtual model that dynamically simulates, predicts, and optimizes battery behavior. These models integrate real‐time data with in‐depth physical insights, offering a comprehensive solution for battery management. Fundamental to this development are advanced characterization techniques such as microscopy, spectroscopy, tomography, and electrochemical methods—that provide critical insights into the underlying physics of batteries. Additionally, machine learning (ML) extends beyond predictive analytics to enhance the analytical capabilities. By uncovering deep physical insights, ML significantly improving the accuracy, reliability, and interpretability of these techniques. This review explores how integrating ML with traditional battery characterization techniques bridges the gap between deep physical insights and data‐driven analysis. The synergy not only enhances precision and computational efficiency but also minimizes human intervention, thereby paving the way for more robust and transparent digital twin technologies in battery research.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Memory and Neural Computing · EEG and Brain-Computer Interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Energy Research Institute @ NTU Nanyang Technological University Singapore 637141 Singapore
School of Material Science and Engineering Nanyang Technological University Singapore 639798 Singapore
Infocomm Technology Cluster Singapore Institute of Technology Singapore 567739 Singapore
Material Science and Engineering Research Group Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung 40132 Indonesia
Department of Chemistry University of Indonesia Depok 16424 Indonesia
Metallurgical & Materials Engineering Department Fakultas Teknik Universitas Indonesia Depok 16424 Indonesia
Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) Singapore 138634 Singapore
Institute of Sustainability for Chemicals Energy and Environment (ISCE2) Agency for Science Technology and Research (A*STAR) Singapore 627833 Singapore
Institute of Science and Technology for New Energy Xi'an Technological University Xi'an 710021 China
Funding
National Research Foundation Singapore · Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi · Agency for Science, Technology and Research
References (100+)
Show source-provided references
1. 10.1016/j.ensm.2023.10278510.1016/j.ensm.2023.102785 ↗
2. 10.1016/j.joule.2021.07.01210.1016/j.joule.2021.07.012 ↗
3. 10.1016/j.joule.2019.11.01810.1016/j.joule.2019.11.018 ↗
4. 10.1038/s42256-020-0156-710.1038/s42256-020-0156-7 ↗
5. 10.1016/j.joule.2021.06.00510.1016/j.joule.2021.06.005 ↗
6. 10.1002/adma.20210147410.1002/adma.202101474 ↗
7. 10.1186/s10033-021-00577-010.1186/s10033-021-00577-0 ↗
8. 10.1002/ange.20210736910.1002/ange.202107369 ↗
9. 10.1002/smtd.20230102110.1002/smtd.202301021 ↗
10. 10.1016/j.apenergy.2021.11734610.1016/j.apenergy.2021.117346 ↗
11. 10.1016/j.rser.2022.11228210.1016/j.rser.2022.112282 ↗
12. 10.1021/acs.chemrev.1c0010810.1021/acs.chemrev.1c00108 ↗
13. 10.1080/00207543.2021.201459110.1080/00207543.2021.2014591 ↗
14. 10.1016/j.matt.2024.04.03010.1016/j.matt.2024.04.030 ↗
15. 10.1021/acsenergylett.2c0199610.1021/acsenergylett.2c01996 ↗
16. A. S.Krishnapriyan A.Gholami S.Zhe R. M.Kirby M. W.Mahoney Characterizing Possible Failure Modes in Physics‐Informed Neural Networks.
17. 10.1039/C9SE00038K10.1039/C9SE00038K ↗
18. 10.1038/s41524-023-01142-010.1038/s41524-023-01142-0 ↗
19. 10.1038/s41563-020-00833-z10.1038/s41563-020-00833-z ↗
20. 10.1021/acsnano.7b0750410.1021/acsnano.7b07504 ↗
21. 10.1016/j.xcrp.2022.10100810.1016/j.xcrp.2022.101008 ↗
22. 10.1016/j.matchar.2021.11090610.1016/j.matchar.2021.110906 ↗
23. 10.1149/1945-7111/abef8410.1149/1945-7111/abef84 ↗
24. Ronneberger O. · 2015 · 234
25. 10.1038/s41598-021-84499-w10.1038/s41598-021-84499-w ↗
26. 10.1021/acs.nanolett.3c0244110.1021/acs.nanolett.3c02441 ↗
27. 10.1038/s41524-022-00905-510.1038/s41524-022-00905-5 ↗
28. 10.1038/s42256-022-00555-810.1038/s42256-022-00555-8 ↗
29. 10.1126/sciadv.adn589910.1126/sciadv.adn5899 ↗
30. 10.1038/s41467-021-26511-510.1038/s41467-021-26511-5 ↗
31. 10.1038/s41524-023-01133-110.1038/s41524-023-01133-1 ↗
32. 10.1016/j.ensm.2023.10303610.1016/j.ensm.2023.103036 ↗
33. Zhao G. · Multimed Tools Appl. · 2018
34. 10.1038/s42256-021-00322-110.1038/s42256-021-00322-1 ↗
35. 10.1038/s41565-022-01081-910.1038/s41565-022-01081-9 ↗
36. 10.1016/j.commatsci.2017.05.01210.1016/j.commatsci.2017.05.012 ↗
37. 10.1038/s41467-020-16233-510.1038/s41467-020-16233-5 ↗
38. 10.1021/acsaem.0c0205310.1021/acsaem.0c02053 ↗
39. 10.1002/adfm.20227021810.1002/adfm.202270218 ↗
40. 10.1038/s41524-022-00709-710.1038/s41524-022-00709-7 ↗
41. 10.1038/s41524-023-01039-y10.1038/s41524-023-01039-y ↗
42. 10.1038/s41467-021-26480-910.1038/s41467-021-26480-9 ↗
43. 10.1038/s41524-020-0340-710.1038/s41524-020-0340-7 ↗
44. 10.1126/science.abm896210.1126/science.abm8962 ↗
45. 10.1038/s41551-022-00914-110.1038/s41551-022-00914-1 ↗
46. 10.1038/s41524-022-00870-z10.1038/s41524-022-00870-z ↗
47. 10.1002/adfm.20210719010.1002/adfm.202107190 ↗
48. 10.1088/0957-4484/20/8/08571410.1088/0957-4484/20/8/085714 ↗
49. 10.1038/s41524-021-00611-810.1038/s41524-021-00611-8 ↗
50. 10.1002/adom.20200180810.1002/adom.202001808 ↗
51. 10.1002/smtd.20170034110.1002/smtd.201700341 ↗
52. 10.1021/acs.jpclett.7b0236410.1021/acs.jpclett.7b02364 ↗
53. 10.1063/1.512659710.1063/1.5126597 ↗
54. 10.1103/PhysRevMaterials.3.03360410.1103/PhysRevMaterials.3.033604 ↗
55. 10.1038/s41524-018-0067-x10.1038/s41524-018-0067-x ↗
56. 10.1016/j.patter.2020.10001310.1016/j.patter.2020.100013 ↗
57. 10.1038/s41524-020-00376-610.1038/s41524-020-00376-6 ↗
58. 10.1038/s41524-021-00664-910.1038/s41524-021-00664-9 ↗
59. 10.1039/D0SC00382D10.1039/D0SC00382D ↗
60. 10.1038/s41524-024-01313-710.1038/s41524-024-01313-7 ↗
61. 10.1038/s41524-024-01408-110.1038/s41524-024-01408-1 ↗
62. 10.1002/adma.20200587810.1002/adma.202005878 ↗
63. 10.1038/s41467-018-06972-x10.1038/s41467-018-06972-x ↗
64. 10.1021/cr800344k10.1021/cr800344k ↗
65. 10.1002/adom.20220310410.1002/adom.202203104 ↗
66. 10.1038/s41524-019-0172-510.1038/s41524-019-0172-5 ↗
67. 10.1038/s41598-019-54770-210.1038/s41598-019-54770-2 ↗
68. 10.1038/s41467-020-15340-710.1038/s41467-020-15340-7 ↗
69. 10.1021/ar200215t10.1021/ar200215t ↗
70. 10.1002/smtd.20190011910.1002/smtd.201900119 ↗
71. 10.1107/S205225251700714X10.1107/S205225251700714X ↗
72. 10.1107/S160057751800487310.1107/S1600577518004873 ↗
73. 10.1038/s41524-019-0196-x10.1038/s41524-019-0196-x ↗
74. N. J.Szymanski C. J.Bartel Y.Zeng Q.Tu A Probabilistic Deep Learning Approach to Automate the Interpretation of Multi‐Phase Diffraction Spectra.
75. 10.1038/s41524-023-00984-y10.1038/s41524-023-00984-y ↗
76. 10.1038/s41524-024-01230-910.1038/s41524-024-01230-9 ↗
77. 10.1038/s41524-023-01164-810.1038/s41524-023-01164-8 ↗
78. 10.1038/s41524-020-00466-510.1038/s41524-020-00466-5 ↗
79. 10.1002/crat.20110012510.1002/crat.201100125 ↗
80. 10.1038/s41467-018-05169-610.1038/s41467-018-05169-6 ↗
81. 10.1126/sciadv.aaw194910.1126/sciadv.aaw1949 ↗
82. 10.1126/science.aay306210.1126/science.aay3062 ↗
83. 10.1038/s41524-021-00539-z10.1038/s41524-021-00539-z ↗
84. 10.1017/S143192761900049710.1017/S1431927619000497 ↗
85. 10.1016/j.ultramic.2021.11325610.1016/j.ultramic.2021.113256 ↗
86. 10.1038/s41524-022-00939-910.1038/s41524-022-00939-9 ↗
87. 10.1038/s41524-018-0139-y10.1038/s41524-018-0139-y ↗
88. 10.1038/s41524-022-00793-910.1038/s41524-022-00793-9 ↗
89. 10.1038/s41524-022-00803-w10.1038/s41524-022-00803-w ↗
90. 10.1002/aenm.20200080610.1002/aenm.202000806 ↗
91. 10.1038/s41598-018-34525-110.1038/s41598-018-34525-1 ↗
92. 10.1063/5.003148610.1063/5.0031486 ↗
93. 10.1038/s41524-021-00644-z10.1038/s41524-021-00644-z ↗
94. 10.1016/j.ensm.2021.12.01910.1016/j.ensm.2021.12.019 ↗
95. 10.1038/s41524-019-0148-510.1038/s41524-019-0148-5 ↗
96. 10.1002/adma.20210917110.1002/adma.202109171 ↗
97. 10.1002/admi.20230064010.1002/admi.202300640 ↗
98. 10.1016/j.egyai.2022.10018810.1016/j.egyai.2022.100188 ↗
99. 10.1007/978-981-99-1027-4_11910.1007/978-981-99-1027-4_119 ↗
100. 10.1016/j.jpowsour.2021.22986010.1016/j.jpowsour.2021.229860 ↗