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Online capacity estimation for lithium-ion batteries through joint estimation method

Author

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  • Yu, Quanqing
  • Xiong, Rui
  • Yang, Ruixin
  • Pecht, Michael G.

Abstract

Accurate capacity estimation of lithium-ion batteries is a crucial challenge, especially in the presence of noise in the acquisition sensors. This paper developed an online capacity estimation technique based on the joint estimation algorithms for lithium-ion batteries. The recursive least squares algorithm is used for parameter identification, and the adaptive H∞ filter is responsible for capacity estimation. In order to solve the problem that the capacity and state of charge will affect each other and cause the convergence speed to slow down, the open circuit voltage at the current sampling instant is expressed as the equation of open circuit voltage and capacity at the previous sampling instant. Therefore, the capacity can be treated as a state, as well as the open circuit voltage, rather than state of charge to be estimated through the adaptive H∞ filter. The capacity estimation error based on recursive least squares and adaptive H∞ filter is also deduced in this study. The simulation results indicate that the estimated capacity can quickly converge to the reference capacity in case the initial parameter values are inaccurate. Moreover, the erroneous initial parameters have a greater impact than the sensor noises on the capacity estimation error.

Suggested Citation

  • Yu, Quanqing & Xiong, Rui & Yang, Ruixin & Pecht, Michael G., 2019. "Online capacity estimation for lithium-ion batteries through joint estimation method," Applied Energy, Elsevier, vol. 255(C).
  • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919315041
    DOI: 10.1016/j.apenergy.2019.113817
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    Citations

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    Cited by:

    1. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
    2. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    3. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    4. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
    6. Bingyu Sang & Zaijun Wu & Bo Yang & Junjie Wei & Youhong Wan, 2024. "Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter," Energies, MDPI, vol. 17(7), pages 1-16, March.
    7. 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).
    8. Wang, Yujie & Chen, Zonghai, 2020. "A framework for state-of-charge and remaining discharge time prediction using unscented particle filter," Applied Energy, Elsevier, vol. 260(C).
    9. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    10. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    11. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    12. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
    13. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
    14. Quanqing Yu & Changjiang Wan & Junfu Li & Lixin E & Xin Zhang & Yonghe Huang & Tao Liu, 2021. "An Open Circuit Voltage Model Fusion Method for State of Charge Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 14(7), pages 1-22, March.
    15. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    16. Quanqing Yu & Changjiang Wan & Junfu Li & Rui Xiong & Zeyu Chen, 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-15, February.
    17. Huang, Huanyang & Meng, Jinhao & Wang, Yuhong & Feng, Fei & Cai, Lei & Peng, Jichang & Liu, Tianqi, 2022. "A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve," Applied Energy, Elsevier, vol. 322(C).
    18. Liu, Yisheng & Fan, Guodong & Zhou, Boru & Chen, Shun & Sun, Ziqiang & Wang, Yansong & Zhang, Xi, 2023. "Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks," Applied Energy, Elsevier, vol. 351(C).
    19. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo & Xie, Jiale, 2022. "A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry," Energy, Elsevier, vol. 239(PC).

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