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Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh
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Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex int... | Find, read and cite all the research you need on Tech Science Press
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“Direct measurement methods assess the SOH by acquiring fundamental battery physical parameters, primarily including capacity measurement, charge-discharge curve analysis, and internal resistance measurement [51-55].”
From The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods, Development Trends and Challenges (2025) · cites this paper as [52]
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“The capacity method quantifies degradation through the ratio of maximum available capacity to nominal capacity, providing precise evaluation but requiring prolonged testing duration.”
From The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods, Development Trends and Challenges (2025) · cites this paper as [52]
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“the method achieves exceptional accuracy (RMSE ≤ 0.007, MAPE ≤ 0.0082) on NASA/CALCE datasets”
From The State of Health Estimation of Lithium-Ion Batteries: A Review of Health Indicators, Estimation Methods, Development Trends and Challenges (2025) · cites this paper as [52]
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Cites
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Cited by
Sorted by citations
Tang, K., Luo, B., Chen, D., Wang, C., Chen, L., Li, F., Cao, Y., & Wang, C. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16080429 · Cited by 68
Wang, W.-X., Zhang, X.-Y., Chen, Z., Zhang, X.-S., Li, Z.-L., & Li, L. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116204 · Cited by 10
Huang, K., Zhang, X., Guo, Y., & Li, M. (2026) · Energy · doi:10.1016/j.energy.2026.139928 · Cited by 6
Zeng, J., Yang, Z., Zhu, H., Chen, Y., Huang, T., Tan, P., Zhou, X., Zhou, M., Zhang, Y., & Zhou, H. (2026) · Energy storage materials · doi:10.1016/j.ensm.2026.105002 · Cited by 6
Self-citationRationalizing machine learning models for real-time health prognosis of lithium-ion batteries
Mallea, M., Àngela Nebot, B., & Mugica, F. (2025) · Array · doi:10.1016/j.array.2025.100577 · Cited by 4
Metadata
- Authors
- Yang, L., Zhang, Y., He, W., & Ma, H.
- Affiliations
- Beijing University of Civil Engineering and Architecture (CN)
- ORCID
- Venue
- Energy Engineering
- Publisher
- Tech Science Press
- Published
- 2025
- Volume
- 122
- Issue
- 1
- Pages
- 243-264
- ISSN
- 0199-8595 (ISSN-L)
- 1546-0118
- OpenAlex
- W4405266046
- Cited by
- 13
- References
- 0
- Citations by year
- 2026: 7 · 2025: 6
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Energy Engineering (publishedVersion, cc-by)
- Funders
- Beijing University of Civil Engineering and Architecture (GJZJ20220802)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- lithium-ion batteries, state of health estimation, remaining useful life prediction, twin support vector machine, battery management systems
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