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Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries

Author

Listed:
  • Dezhi Li

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

  • Dongfang Yang

    (Xi’an Traffic Engineering Institute, Xi’an 710300, China)

  • Liwei Li

    (School of Control Science and Engineering, Shandong University, Jinan 250061, China)

  • Licheng Wang

    (School of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Kai Wang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

Abstract

The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision.

Suggested Citation

  • Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6665-:d:913208
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    References listed on IDEAS

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    1. Zhenhua Cui & Jiyong Dai & Jianrui Sun & Dezhi Li & Licheng Wang & Kai Wang & A. M. Bastos Pereira, 2022. "Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    2. Mc Carthy, Kieran & Gullapalli, Hemtej & Kennedy, Tadhg, 2022. "Online state of health estimation of Li-ion polymer batteries using real time impedance measurements," Applied Energy, Elsevier, vol. 307(C).
    3. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    4. Li, Dezhi & Li, Shuo & Zhang, Shubo & Sun, Jianrui & Wang, Licheng & Wang, Kai, 2022. "Aging state prediction for supercapacitors based on heuristic kalman filter optimization extreme learning machine," Energy, Elsevier, vol. 250(C).
    5. Zhenxiao Yi & Kun Zhao & Jianrui Sun & Licheng Wang & Kai Wang & Yongzhi Ma & Ali Ahmadian, 2022. "Prediction of the Remaining Useful Life of Supercapacitors," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, May.
    6. Che, Yunhong & Deng, Zhongwei & Li, Penghua & Tang, Xiaolin & Khosravinia, Kavian & Lin, Xianke & Hu, Xiaosong, 2022. "State of health prognostics for series battery packs: A universal deep learning method," Energy, Elsevier, vol. 238(PB).
    7. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    8. Yaping Fu & Hongfeng Wang & Guangdong Tian & Zhiwu Li & Hesuan Hu, 2019. "Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2257-2272, June.
    9. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    10. Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    11. Hanlei Sun & Jianrui Sun & Kun Zhao & Licheng Wang & Kai Wang & Mohammad Yaghoub Abdollahzadeh Jamalabadi, 2022. "Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, March.
    12. Yu Hua & Na Wang & Keyou Zhao, 2021. "Simultaneous Unknown Input and State Estimation for the Linear System with a Rank-Deficient Distribution Matrix," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, January.
    13. Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
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    5. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
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