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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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    2. Gabriele Sordi & Claudio Rabissi & Andrea Casalegno, 2023. "Reliable Thermal-Physical Modeling of Lithium-Ion Batteries: Consistency between High-Frequency Impedance and Ion Transport," Energies, MDPI, vol. 16(12), pages 1-17, June.
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