Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion driving Article Swipe
Chao Lyu
,
Shaochun Xu
,
Junfu Li
,
Michael Pecht
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.geits.2024.100162
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.geits.2024.100162
Lithium-ion batteries have been rapidly developed as clean energy sources in many industrial fields, such as new energy vehicles and energy storage. The core issues hindering their further promotion and application are reliability and safety. A digital twin model that maps onto the physical entity of the battery with high simulation accuracy helps to monitor internal states and improve battery safety. This work focuses on developing a digital twin model via a mechanism-data-driven parameter updating algorithm to increase the simulation accuracy of the internal and external characteristics of the full-time domain battery under complex working conditions. An electrochemical model is first developed with the consideration of how electrode particle size impacts battery characteristics. By adding the descriptions of temperature distribution and particle-level stress, a multi-particle size electrochemical-thermal-mechanical coupling model is established. Then, considering the different electrical and thermal effect among individual cells, a model for the battery pack is constructed. A digital twin model construction method is finally developed and verified with battery operating data.
Related Topics
Concepts
Battery (electricity)
Reliability (semiconductor)
Battery pack
Lithium-ion battery
Mechanism (biology)
Computer science
Lithium (medication)
Energy storage
Work (physics)
Energy (signal processing)
Simulation
Automotive engineering
Engineering
Mechanical engineering
Power (physics)
Medicine
Physics
Mathematics
Statistics
Philosophy
Quantum mechanics
Epistemology
Endocrinology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.geits.2024.100162
- OA Status
- diamond
- Cited By
- 10
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390843865
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390843865Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.geits.2024.100162Digital Object Identifier
- Title
-
Digital twin modeling method for lithium-ion batteries based on data-mechanism fusion drivingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-13Full publication date if available
- Authors
-
Chao Lyu, Shaochun Xu, Junfu Li, Michael PechtList of authors in order
- Landing page
-
https://doi.org/10.1016/j.geits.2024.100162Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.geits.2024.100162Direct OA link when available
- Concepts
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Battery (electricity), Reliability (semiconductor), Battery pack, Lithium-ion battery, Mechanism (biology), Computer science, Lithium (medication), Energy storage, Work (physics), Energy (signal processing), Simulation, Automotive engineering, Engineering, Mechanical engineering, Power (physics), Medicine, Physics, Mathematics, Statistics, Philosophy, Quantum mechanics, Epistemology, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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