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Physics-informed machine learning for accurate SOH estimation of lithium-ion batteries considering various temperatures and operating conditions

Author

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  • Lin, Chunsong
  • Tuo, Xianguo
  • Wu, Longxing
  • Zhang, Guiyu
  • Lyu, Zhiqiang
  • Zeng, Xiangling

Abstract

Accurate State of Health (SOH) estimation for lithium batteries (LIBs) is crucial for the safe operation of battery systems. However, the lack of physical properties and the varied operating conditions in real-world use further increase the difficulty of traditional SOH estimation, making it a significant challenge in current research. For this reason, this paper proposes a physics-informed machine learning (PIML) method for accurate SOH estimation of LIBs varied operating conditions. Considering the fully charged relaxation voltage data obtained easily in practical applications, firstly, this paper discussed the relaxation voltage data related to the battery's aging characteristics from the experimental tests. Secondly, the fractional-order equivalent circuit model (FOECM) is constructed and parameters characterizing battery degradation are identified for extracting the physical features. Ultimately, a novel PIML framework based FOECM of LIB is developed, then the datasets of three different battery types under different temperatures and discharge rates are used and validated for SOH estimation without considering any usage information. Experimental results show that the PIML method proposed in this paper can quickly achieve SOH estimation and keep the accuracy in 0.84 % for different types of batteries under varying experimental conditions. In addition, compared with other feature extraction methods, the PIML-based SOH estimation has obvious advantages with 16.2 %, which provides an important reference for the design and optimization of advanced battery management systems.

Suggested Citation

  • Lin, Chunsong & Tuo, Xianguo & Wu, Longxing & Zhang, Guiyu & Lyu, Zhiqiang & Zeng, Xiangling, 2025. "Physics-informed machine learning for accurate SOH estimation of lithium-ion batteries considering various temperatures and operating conditions," Energy, Elsevier, vol. 318(C).
  • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225005791
    DOI: 10.1016/j.energy.2025.134937
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    1. Pang, Hui & Chen, Kaiqiang & Geng, Yuanfei & Wu, Longxing & Wang, Fengbin & Liu, Jiahao, 2024. "Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter," Energy, Elsevier, vol. 293(C).
    2. Huang, Xucong & Peng, Zhaoqin & Tang, Diyin & Chen, Juan & Zio, Enrico & Zheng, Zaiping, 2024. "A physics-informed autoencoder for system health state assessment based on energy-oriented system performance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    3. Zuo, Hongyan & Liang, Jingwei & Zhang, Bin & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2023. "Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction," Energy, Elsevier, vol. 282(C).
    4. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
    6. Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    8. Yang, Bowen & Wang, Dafang & Sun, Xu & Chen, Shiqin & Wang, Xingcheng, 2023. "Offline order recognition for state estimation of Lithium-ion battery using fractional order model," Applied Energy, Elsevier, vol. 341(C).
    9. Braco, Elisa & San Martín, Idoia & Sanchis, Pablo & Ursúa, Alfredo, 2023. "Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles," Applied Energy, Elsevier, vol. 329(C).
    10. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    11. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    12. Hu, Minghui & Li, Yunxiao & Li, Shuxian & Fu, Chunyun & Qin, Datong & Li, Zonghua, 2018. "Lithium-ion battery modeling and parameter identification based on fractional theory," Energy, Elsevier, vol. 165(PB), pages 153-163.
    13. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    15. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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