Scalable carbon-patterned layer enhances low-temperature performance of large-format lithium-ion batteries
- Authors
- Lim Jaejin; Park Siyoung; Lee Hyobin; 최승엽; Nam Gwonsik; Kim Kyung-Geun; Choi Jaecheol; Lee Young-Gi; Lee Yong Min
- Issue Date
- Jun-2025
- Publisher
- Elsevier BV
- Citation
- Journal of Energy Chemistry, v.105, pp 87 - 95
- Pages
- 9
- Journal Title
- Journal of Energy Chemistry
- Volume
- 105
- Start Page
- 87
- End Page
- 95
- URI
- https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/23370
- DOI
- 10.1016/j.jechem.2025.01.046
- ISSN
- 2095-4956
2096-885X
- Abstract
- With electric vehicles (EVs) emerging as a primary mode of transportation, ensuring their reliable operation in harsh environments is crucial. However, lithium-ion batteries (LIBs) suffer from severe polarization at low temperatures, limiting their operation in cold climates. In addition, difficulties in discovering new battery materials have highlighted a growing demand for innovative electrode designs that achieve high performance, even at low temperatures. To address this issue, we prepared a thin, resistive, and patterned carbon interlayer on the anode current collector. This carbon-patterned layer (CPL) serves as a self-heating layer to efficiently elevate the entire cell temperature, thus improving the rate capability and cyclability at low temperatures while maintaining the performance at room temperature. Furthermore, we validated the versatile applicability of CPLs to large-format LIB cells through experimental studies and electrochemo-thermal multiphysics modeling and simulations, with the results confirming 11% capacity enhancement in 21,700 cylindrical cells at a 0.5C-rate and -24 degrees C. We expect this electrode design to offer reliable power delivery in harsh climates, thereby potentially expanding the applications of LIBs. (c) 2025 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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