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A Pulse-Test-Based Method for Predicting Lithium Battery DRT Curves and State Estimation.

Chenguang Zhou1,2, Jianhui Su1,2, Jidong Lai1

  • 1School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui 230009, China.

ACS Applied Materials & Interfaces
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a data-driven method using an encoder-decoder network to calculate lithium battery Distribution of Relaxation Times (DRT) curves. This approach overcomes limitations of traditional methods, improving accuracy and enabling better state of charge estimation.

Keywords:
DRT curvesencoder–decoder networklithium-ion batteriesmultihead self-attentionpulse curves

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Data Science

Background:

  • Distribution of Relaxation Times (DRT) curves are crucial for analyzing lithium battery internal dynamics, polarization, and state estimation.
  • Traditional DRT calculation methods using pulse data are limited by device sampling rates and shelving times, restricting analysis to higher frequencies.
  • Accurate DRT analysis is essential for understanding battery performance and degradation.

Purpose of the Study:

  • To develop a data-driven methodology for deriving battery DRT curves that overcomes the limitations of traditional pulse-based methods.
  • To enhance the accuracy and prediction range of DRT curves, particularly in middle- and low-frequency impedance ranges.
  • To validate the utility of predicted DRT curves for state of charge (SOC) estimation and analysis of polarization reactions.

Main Methods:

  • Employed a data-driven methodology to derive DRT curves, circumventing sampling frequency and shelving time constraints.
  • Developed an encoder-decoder neural network integrating Convolutional Neural Networks (CNNs), Long-Short-Term Memory (LSTM) networks, and Multihead Self-Attention (MSA) mechanisms.
  • Validated the proposed method using experimental and publicly available lithium battery datasets.

Main Results:

  • Achieved a broader prediction range for DRT curves, extending into middle- and low-frequency impedance ranges.
  • The developed neural network significantly enhanced the accuracy of DRT curve predictions.
  • Predicted DRT curves demonstrated high accuracy in corroborating state of charge (SOC) estimation, closely aligning with results from actual DRT curves.

Conclusions:

  • The proposed data-driven method effectively generates accurate DRT curves, overcoming limitations of conventional techniques.
  • The integrated neural network architecture excels at capturing temporal and spatial features, improving DRT prediction.
  • This approach provides a valuable tool for analyzing polarization reactions and their correlation with SOC in lithium batteries.