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A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

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  • Qiaohua Fang

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Xuezhe Wei

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Tianyi Lu

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Haifeng Dai

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Jiangong Zhu

    (Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
    Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Germany)

Abstract

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li 1 (NiCoAl) 1 O 2 -Li 1 (NiCoMn) 1 O 2 /graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α -based, β -based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.

Suggested Citation

  • Qiaohua Fang & Xuezhe Wei & Tianyi Lu & Haifeng Dai & Jiangong Zhu, 2019. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model," Energies, MDPI, vol. 12(7), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1349-:d:220983
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    References listed on IDEAS

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    2. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    3. Wenzhe Li & Youhang Zhou & Haonan Zhang & Xuan Tang, 2023. "A Review on Battery Thermal Management for New Energy Vehicles," Energies, MDPI, vol. 16(13), pages 1-20, June.
    4. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries," Applied Energy, Elsevier, vol. 357(C).
    5. Ashleigh Townsend & Rupert Gouws, 2023. "A Comparative Review of Capacity Measurement in Energy Storage Devices," Energies, MDPI, vol. 16(10), pages 1-26, May.
    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. Hao Fan & Lan Wang & Wei Chen & Bin Liu & Pengxin Wang, 2023. "A J-Type Air-Cooled Battery Thermal Management System Design and Optimization Based on the Electro-Thermal Coupled Model," Energies, MDPI, vol. 16(16), pages 1-19, August.
    8. Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    9. Chuan-Wei Zhang & Shang-Rui Chen & Huai-Bin Gao & Ke-Jun Xu & Zhan Xia & Shuai-Tian Li, 2019. "Study of Thermal Management System Using Composite Phase Change Materials and Thermoelectric Cooling Sheet for Power Battery Pack," Energies, MDPI, vol. 12(10), pages 1-14, May.

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