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Research on online passive electrochemical impedance spectroscopy and its outlook in battery management

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  • Yang, Bowen
  • Wang, Dafang
  • Yu, Beike
  • Wang, Facheng
  • Chen, Shiqin
  • Sun, Xu
  • Dong, Haosong

Abstract

Current lithium-ion battery (LIB) management technique relying solely on the limited time-domain measurements appears to reach its limit, and incorporating new sensing information, particularly the impedance of LIB, offers a promising path for improvement. Using the non-stationary random driving profile of electric vehicle (EV), a method to extract online passive electrochemical impedance spectroscopy (OPEIS) is proposed and validated, with relevant factors influencing its efficacy also investigated. The particularity of actual driving profile is revealed both theoretically and experimentally, and beyond expectation, the highly differentiated driving profiles yield a similar spectral pattern, which facilitates the acquisition of OPEIS. Continuous OPEIS characterized by the distribution of relaxation time (DRT) ranging from 0.2 Hz to 3 kHz are analyzed in detail under different battery conditions. Compared with offline reference EIS, most of the measurement errors are <3%. Based on the acquired spectral information and OPEIS, a safety-relevant detector and an internal temperature estimator for LIB are presented, and they can both realize a near instant electrochemical sensing up to 1 Hz. As the underlying opportunities of OPEIS are outlined, challenges in its reliable acquisition and engineering implementation are evaluated as well. By comprehensively discussing the realization of OPEIS, research in this paper is expected to provide valuable references for a more effective battery management.

Suggested Citation

  • Yang, Bowen & Wang, Dafang & Yu, Beike & Wang, Facheng & Chen, Shiqin & Sun, Xu & Dong, Haosong, 2024. "Research on online passive electrochemical impedance spectroscopy and its outlook in battery management," Applied Energy, Elsevier, vol. 363(C).
  • Handle: RePEc:eee:appene:v:363:y:2024:i:c:s030626192400429x
    DOI: 10.1016/j.apenergy.2024.123046
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    References listed on IDEAS

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    1. Zheng, Linfeng & Zhang, Lei & Zhu, Jianguo & Wang, Guoxiu & Jiang, Jiuchun, 2016. "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Elsevier, vol. 180(C), pages 424-434.
    2. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. Michail Tsagris & Abdulaziz Alenazi & Connie Stewart, 2023. "Flexible Non-parametric Regression Models for Compositional Response Data with Zeros," Working Papers 2306, University of Crete, Department of Economics.
    4. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    5. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    6. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    7. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    8. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    9. Edoardo Locorotondo & Fabio Corti & Luca Pugi & Lorenzo Berzi & Alberto Reatti & Giovanni Lutzemberger, 2021. "Design of a Wireless Charging System for Online Battery Spectroscopy," Energies, MDPI, vol. 14(1), pages 1-17, January.
    10. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
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