A deep learning approach for state-of-health estimation of lithium-ion batteries based on differential thermal voltammetry and attention mechanism

Bosong Zou, Huijie Wang, Tianyi Zhang, Mengyu Xiong, Chang Xiong, Qi Sun, Wentao Wang, Lisheng Zhang, Cheng Zhang, Haijun Ruan

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
35 Downloads (Pure)

Abstract

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have shown excellent performance in estimating SOH, but obtaining high-quality and strongly correlated features remains a major challenge for these methods. Moreover, different features have varying importance in both spatial and temporal scales, and single data-driven models are unable to capture this information, leading to issues with attention dispersion. In this paper, we propose a data-driven method for SOH estimation leveraging the Bi-directional Long Short-Term Memory (Bi-LSTM) that uses the Differential Thermal Voltammetry (DTV) analysis to extract features, and incorporates attention mechanisms (AM) at both temporal and spatial scales to enable the model focusing on important information in the features. The proposed method is validated using the Oxford Battery degradation Dataset, and the results show that it achieves high accuracy and robustness in SOH estimation. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are around 0.4% and 0.3%, respectively, indicating the potential for online application of the proposed method in the cyber hierarchy and interactional network (CHAIN) framework.
Original languageEnglish
Article number1178151
Number of pages14
JournalFrontiers in Energy Research
Volume11
Early online date14 Jun 2023
DOIs
Publication statusE-pub ahead of print - 14 Jun 2023

Bibliographical note

© 2023 Zou, Wang, Zhang, Xiong, Xiong, Sun, Wang, Zhang, Zhang and Ruan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Funder

This work was financially supported by Young elite scientist sponsorship program by China Association for Science and Technology (No. YESS20200066).

Keywords

  • feature signal analysis
  • attention mechanism
  • state of health
  • data-driven
  • Bi-directional long short-term memory

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