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Early Prediction of Battery Lifetime for Lithium-Ion Batteries Based on a Hybrid Clustered Cnn Model

https://doi.org/10.2139/ssrn.4848212
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Life prediction model for lithium-ion battery considering fast-charging protocol
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Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory
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Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning
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Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning
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Battery Lifetime Prognostics
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Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model
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Battery health estimation with degradation pattern recognition and transfer learning
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Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection
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The lithium-ion battery: State of the art and future perspectives
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Performance and cost of materials for lithium-based rechargeable automotive batteries
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Data-driven prediction of battery cycle life before capacity degradation
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Squeeze-and-Excitation Networks
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A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life
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A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life
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Automated Feature Extraction and Selection for Data-Driven Models of Rapid Battery Capacity Fade and End of Life
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Remaining Useful Life Early Prediction of Batteries Based on the Differential Voltage and Differential Capacity Curves
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Degradation Curve Prediction of Lithium-Ion Batteries Based on Knee Point Detection Algorithm and Convolutional Neural Network
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Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
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Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction
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Squeeze-and-Excitation Networks
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