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Data-driven State of Health Estimation for Sodium-ion Batteries
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Nan LU1, Yue SUN1, Peng PENG1, 2, Rui XIONG1, Fengchun SUN1
Journal of Power Supply | 2024, 22(1) : 1 - 10
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Journal of Power Supply | 2024, 22(1): 1-10
• Invited Manuscript •
Data-driven State of Health Estimation for Sodium-ion Batteries
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Nan LU1, Yue SUN1, Peng PENG1, 2, Rui XIONG1, Fengchun SUN1
Affiliations
  • 1 School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China
  • 2 CSG PGC Energy Storage Research Institute Guangzhou 510630 China
Published: 2024-01-30 doi: 10.13234/j.issn.2095-2805.2024.1.1
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The state of health (SOH) estimation for sodium-ion batteries is crucial for their safe and efficient applications, which is also a key to large-scale energy storage implementations. However, sodium-ion batteries exhibit usage-induced degradation with unclear mechanisms and are sensitive to operating conditions and environmental factors, posing a challenge to the accurate SOH estimation. In this paper, a data-driven SOH estimation method for sodium-ion batteries is proposed. The charging data is correlated with capacity degradation, and variance filtering, grey relational analysis and recursive feature elimination are integrated for feature selection. In addition, four machine learning methods including multiple linear regression, support vector machine, Gaussian process regression and error back propagation neural network are applied to formulate the corresponding estimation methods. Test results reveal that the root mean square errors for the four methods are all less than 1.6%, with Gaussian process regression showing an error rate below 0.8%, indicating a precise SOH estimation for sodium-ion batteries.

Sodium-ion battery  /  state of health(SOH)  /  data driven  /  aging feature  /  machine learning
Nan LU, Yue SUN, Peng PENG, Rui XIONG, Fengchun SUN. Data-driven State of Health Estimation for Sodium-ion Batteries[J]. Journal of Power Supply, 2024 , 22 (1) : 1 -10 . DOI: 10.13234/j.issn.2095-2805.2024.1.1
Funding
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  • Key Project of CSG(STKJXM20210104)
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Year 2024 volume 22 Issue 1
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Article Info
doi: 10.13234/j.issn.2095-2805.2024.1.1
  • Receive Date：2024-01-03
  • Online Date：2025-07-09
  • Published：2024-01-30
Article Data
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History
  • Received：2024-01-03
  • Revised：2024-01-29
  • Accepted：2024-01-29
Funding
Key Project of CSG(STKJXM20210104)
Affiliations
    1 School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China
    2 CSG PGC Energy Storage Research Institute Guangzhou 510630 China
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https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2024.1.1
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表12种不同金属材料的力学参数
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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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