Your Location:
Home >
Browse articles >
State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm
Power Generation Technology Vol. 46, Issue 6, Pages: 1154-1163(2025)
Energy Storage|Updated:2026-06-29
|
State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm
- “A lithium-ion battery SOH estimation method considering battery capacity recovery was proposed, and its research progress in the field of battery management was introduced. The team established a data preprocessing system that integrates median absolute deviation and Savitzky Golay filtering, and explored the topic of improving the adaptive honey badger algorithm to optimize the time convolutional network. This provides a solution to the problem of traditional methods neglecting capacity recovery and insufficient feature effectiveness.”
DOI:10.12096/j.2096-4528.pgt.24068
Revised:2024-07-20
Scan QR Code
5
Views
6
Downloads
0
CSCD
Alert me when the article has been cited
Submit
Related Articles
Virtual Inertia Control Strategy for Direct Current Bus Voltage in Integrated Energy Systems Based on Electric-Hydrogen Hybrid Energy Storage
Frequency Regulation Control Strategy for Flywheel Energy Storage Coupled With Thermal Power Units Under Multiple Operation Stages
Review of Research on Wind-Solar-Hydro Complementary Power Generation Systems
Recent Developments on the Application of Chloride Molten Salt Heat Storage Technology to New Energy Power Generation
A Frequency Regulation Method of Energy Storage System Based on Adaptive Adjustment of Virtual Synchronous Generator Control Parameters
Related Author
XUE Sheng
DAI Linhao
LI Jinjian
HONG Feng
LIANG Boyang
SUN Fengdong
LIANG Lu
WANG Wei
Related Institution
College of Electrical and Information Engineering, Lanzhou University of Technology
School of Control and Computer Engineering, North China Electric Power University, Changping District
Chengdu Chengdian Electric Power Engineering Design Co., Ltd.
Tangshan Institute, Southwest Jiaotong University
School of Energy and Power Engineering, Changsha University of Science and Technology

