Your Location:
Home >
Browse articles >
State of Health Estimation for Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy
Power Generation Technology   Vol. 47, Issue 1, Pages: 133-144(2026)
Energy Storage|Updated:2026-05-15
  • Export

  • Collection

  • Album

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

    • “The high-precision SOH prediction method based on electrochemical impedance spectroscopy data "introduces its research progress in the field of battery health management. The research team has established a CNN BiLSTM Attention model system to provide a solution for improving the accuracy of battery life prediction”
    • DOI:10.12096/j.2096-4528.pgt.260112    

      CLC:
    • Revised:2025-07-15,

    Scan QR Code

  •  
  •  

19

Views

11

Downloads

0

CSCD

Alert me when the article has been cited
Submit
Outline

Related Articles

Research on Short-Term Photovoltaic Power Prediction Method Based on an Improved Rime Optimization Algorithm
Variable Step Power Tracking Control Strategy for Expander-Generator Systems in Distributed Compressed Air Energy Storage
Virtual Inertia Control Strategy for Direct Current Bus Voltage in Integrated Energy Systems Based on Electric-Hydrogen Hybrid Energy Storage
Multi-User Short-Term Load Prediction Method Based on Frequency-Domain Enhanced Transformer
Optimal Planning of Electric Vehicle Charging Station Based on Spatio-Temporal Distribution Prediction of Charging Demand

Related Author

WANG Lingzhi
ZHAO Jiarui
LI Wanjun
LI Jie
ZHANG Xiong
LÜ Jingbo
ZHOU Acheng
LIU Hanchen

Related Institution

School of Artificial Intelligence, School of Automation, Xi’an University of Posts and Telecommunications
School of Automation Engineering, Xi’an Aeronautical Polytechnic Institute
Huaneng Renewables Co., Ltd. Mengxi Branch, Hohhot 010020, Inner Mongolia Autonomous Region
School of Mechanical Engineering, Qinghai University
Department of Electrical Engineering, Tsinghua University, Haidian District