ArticleScientific reports2024
Application of state of health estimation and remaining useful life prediction for lithium-ion batteries based on AT-CNN-BiLSTM.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning-based RUL and SOH prediction of lithium-ion batteries using LOCO.Scientific reports · 2026Article
- Hybrid multi-scale CNN-Residual-LSTM approach for robust state-of-charge estimation in lithium-ion batteries.Scientific reports · 2026Article
- An intelligent cloud-based battery management framework integrating GRU state-of-health estimation and double DQN charging optimisation.Scientific reports · 2026Article
- Hydraulic support pressure prediction via deep learning with multilevel temporal feature integration.Scientific reports · 2025Article
- CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance.Scientific reports · 2025Article
- Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm.Scientific reports · 2025Article
- A hybrid approach for lithium-ion battery remaining useful life prediction using signal decomposition and machine learning.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ensuring the long-term safe usage of lithium-ion batteries hinges on accurately estimating the State of Health [Formula: see text] and predicting the Remaining Useful Life (RUL). This study proposes a novel prediction method based on a AT-CNN-BiLSTM architecture. Initially, key parameters such as voltage, current, temperature, and SOH are extracted and averaged for each cycle to ensure the uniformity and reliability of the input data. The CNN is utilized to extract deep features from the data, followed by BiLSTM to analyze the temporal dependencies in the data sequences. Since multidimensional parameter data are used to predict the SOH trend of lithium-ion batteries, an attention mechanism is employed to enhance the weight of highly relevant vectors, improving the model's analytical capabilities. Experimental results demonstrate that the CNN-BiLSTM-Attention model achieves an absolute error of 0 in RUL prediction, an [Formula: see text] value greater than 0.9910 , and a MAPE value less than 0.9003 . Comparative analysis with hybrid neural network algorithms such as LSTM, BiLSTM, and CNN-LSTM confirms the proposed model's high accuracy and stability in SOH estimation and RUL prediction.
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