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Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope

Author

Listed:
  • Xiong, Wei
  • Xie, Fang
  • Xu, Gang
  • Li, Yumei
  • Li, Ben
  • Mo, Yimin
  • Ma, Fei
  • Wei, Keke

Abstract

As electric vehicles become more common there is increasing concern regarding electric vehicles battery disposal. Considering resource savings and environmental friendliness, reuse rather than disposal of retired batteries is considered to be the most suitable solution. For safely and efficiently reusing retired batteries, a precise state of charge estimation over a wide temperature range and battery degradation scope is crucial. The primary content of this work is described as follows. (1) To obtain accurate closed-loop state estimation, a model that is dependent upon the temperature and aging state of the retired battery is developed. (2) Noise can affect the accuracy of model parameter identification and state of charge estimation, regardless of whether current or voltage measurements are corrupted. As a result of the state of charge estimation error, the reliability of reusing retired batteries will be greatly reduced. To solve this problem and improve the state of charge estimation precision, we propose a co-estimation method employing recursive restricted total least squares for parameter identification and adaptive H-infinity filters for state estimation. (3) In the range of 0 °C–45 °C, the proposed method is completely validated within the state of health range of 80%–50%. Specifically, a comparison is carried out between the proposed method and the H-infinity filter to demonstrate its superiority. In addition, the reliability of the co-estimation method is evaluated in different practical application scenarios. The results show that the proposed co-estimation method is highly robust, and the error in the state of charge estimation is restricted to 1% over a wide temperature range and battery degradation range.

Suggested Citation

  • Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123011928
    DOI: 10.1016/j.renene.2023.119277
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    References listed on IDEAS

    as
    1. Zhu, Rui & Duan, Bin & Zhang, Junming & Zhang, Qi & Zhang, Chenghui, 2020. "Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter," Applied Energy, Elsevier, vol. 277(C).
    2. Al-Wreikat, Yazan & Attfield, Emily Kate & Sodré, José Ricardo, 2022. "Model for payback time of using retired electric vehicle batteries in residential energy storage systems," Energy, Elsevier, vol. 259(C).
    3. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    4. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    5. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.
    6. Boyle, James & Littler, Timothy & Foley, Aoife, 2020. "Battery energy storage system state-of-charge management to ensure availability of frequency regulating services from wind farms," Renewable Energy, Elsevier, vol. 160(C), pages 1119-1135.
    7. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    8. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
    9. Gao, Yang & Zhang, Jialiang & Chen, Yongqiang & Wang, Ling & Wang, Chengyan, 2023. "Graphite regenerating from retired (LFP) lithium-ion battery: Phase transformation mechanism of impurities in low-temperature sulfation roasting process," Renewable Energy, Elsevier, vol. 204(C), pages 290-299.
    10. Maheshwari, A. & Nageswari, S., 2022. "Real-time state of charge estimation for electric vehicle power batteries using optimized filter," Energy, Elsevier, vol. 254(PB).
    11. Li, Mingheng, 2017. "Li-ion dynamics and state of charge estimation," Renewable Energy, Elsevier, vol. 100(C), pages 44-52.
    12. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    13. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    14. Yongcun Fan & Haotian Shi & Shunli Wang & Carlos Fernandez & Wen Cao & Junhan Huang, 2021. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation," Energies, MDPI, vol. 14(8), pages 1-18, April.
    15. Geng, Jingxuan & Gao, Suofen & Sun, Xin & Liu, Zongwei & Zhao, Fuquan & Hao, Han, 2022. "Potential of electric vehicle batteries second use in energy storage systems: The case of China," Energy, Elsevier, vol. 253(C).
    16. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    17. Chuan-Xiang Yu & Yan-Min Xie & Zhao-Yu Sang & Shi-Ya Yang & Rui Huang, 2019. "State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation," Energies, MDPI, vol. 12(21), pages 1-19, October.
    18. Quan Ouyang & Rui Ma & Zhaoxiang Wu & Guotuan Xu & Zhisheng Wang, 2020. "Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification," Energies, MDPI, vol. 13(18), pages 1-14, September.
    19. Li, Yunjian & Li, Kuining & Xie, Yi & Liu, Jiangyan & Fu, Chunyun & Liu, Bin, 2020. "Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current–constant voltage charging strategy," Renewable Energy, Elsevier, vol. 146(C), pages 2688-2699.
    20. Nanaki, Evanthia A. & Koroneos, Christopher J., 2016. "Climate change mitigation and deployment of electric vehicles in urban areas," Renewable Energy, Elsevier, vol. 99(C), pages 1153-1160.
    21. Wei Xiong & Yimin Mo & Cong Yan, 2020. "Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, August.
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    Cited by:

    1. Xiong, Xin & Wang, Yujie & Jiang, Cong & Zhang, Xingchen & Xiang, Haoxiang & Chen, Zonghai, 2024. "End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data," Energy, Elsevier, vol. 292(C).
    2. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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