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September 20, 2025Journal of The Electrochemical SocietyOpen Access

State of Health Estimation for Lithium-Ion Batteries Using Hybrid Signal Decomposition and Deep Feature Fusion

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Authors

ZRZhenxing RenWYWei Yu

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Overview

This study demonstrates enhanced state of health prediction in lithium-ion batteries using signal decomposition and deep feature fusion.

Key Points

  • The proposed method improves state of health prediction accuracy for lithium-ion batteries.
  • Advanced signal decomposition techniques like ICEEMDAN and VMD are combined for better feature extraction.
  • A Transformer-BiLSTM network with cross-attention significantly enhances feature integration and temporal learning.
  • Evaluation on public datasets shows the method outperforms traditional baseline models in accuracy.

Cite This Study

Ren et al. (2025) studied this question.

synapsesocial.com/papers/68d46aa631b076d99fa6754chttps://doi.org/10.1149/1945-7111/ae0983
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Also Consider

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  1. 1State of Health Estimation Method for Lithium-Ion Batteries Based on BiLSTM-Transformer and Fusion Features2025 · 3 citations
  2. 2State of Health Estimation for Lithium‐Ion Batteries via Multisource Health Feature Fusion and Transformers2026
  3. 3State‐of‐Health Estimation of Lithium‐Ion Battery Based on Partial Charge Data and Multimodal Image Fusion2026
  4. 4State of Health Estimation for Lithium-Ion Batteries Based on Multi-Scale Frequency Feature and Time-Domain Feature Fusion Method2024 · 4 citations
  5. 5Lithium-Ion Battery State of Health Prediction Using a Hybrid BiLSTM–Random Forest Framework2026 · 1 citations