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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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    1. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    2. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    3. Jinsong Yu & Baohua Mo & Diyin Tang & Jie Yang & Jiuqing Wan & Jingjing Liu, 2017. "Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use," Energies, MDPI, vol. 10(12), pages 1-19, December.
    4. Taichun Qin & Shengkui Zeng & Jianbin Guo & Zakwan Skaf, 2016. "A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena," Energies, MDPI, vol. 9(11), pages 1-18, November.
    5. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    6. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2017. "Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes," Energy, Elsevier, vol. 135(C), pages 257-268.
    7. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    8. Ngoc-Tham Tran & Abdul Basit Khan & Woojin Choi, 2017. "State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation," Energies, MDPI, vol. 10(1), pages 1-18, January.
    9. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    10. Bizhong Xia & Rui Huang & Zizhou Lao & Ruifeng Zhang & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm," Energies, MDPI, vol. 11(11), pages 1-19, November.
    11. Tiansi Wang & Lei Pei & Tingting Wang & Rengui Lu & Chunbo Zhu, 2015. "On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries," Energies, MDPI, vol. 8(8), pages 1-15, August.
    12. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    13. Jie Yang & Chunyu Du & Ting Wang & Yunzhi Gao & Xinqun Cheng & Pengjian Zuo & Yulin Ma & Jiajun Wang & Geping Yin & Jingying Xie & Bo Lei, 2018. "Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model," Energies, MDPI, vol. 11(12), pages 1-15, December.
    14. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
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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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