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Bottleneck-gated physics-informed neural network for lithium-ion battery SOH estimation from partial charging profiles

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Interpretable deep learning with uncertainty quantification for lithium-ion battery SOH estimation
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An attentional deep learning model based on improved crested porcupine optimizer for state of health prediction of lithium-ion batteries
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Hybrid experts-decoupled and physics-informed neural network for lithium-ion battery degradation modeling and prognosis
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Correlation-aware fusion model for robust SOH estimation in lithium-ion batteries
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Failure mechanisms and risk assessment methodologies for lithium-ion batteries: A comprehensive review
inferredcites →https://doi.org/10.1016/j.eswa.2023.123123openalex-snapshot
A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application
inferredcites →https://doi.org/10.1016/j.isci.2022.103744openalex-snapshot
Connecting battery technologies for electric vehicles from battery materials to management
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges
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Next-generation battery safety management: Machine learning assisted life-time prediction and performance enhancement
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Towards practical data-driven battery state of health estimation: Advancements and insights targeting real-world data
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A comprehensive review of remaining useful life prediction methods for lithium-ion batteries: Models, trends, and engineering applications
inferredcites →https://doi.org/10.1016/j.jechem.2025.08.093openalex-snapshot
A generalizable physics-informed neural network for lithium-ion battery SOH estimation utilizing partial charging segments
inferredcites →https://doi.org/10.1016/j.jpowsour.2011.08.040openalex-snapshot
Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method
inferredcites →https://doi.org/10.1016/j.rser.2024.114577openalex-snapshot
Lithium-ion battery digitalization: Combining physics-based models and machine learning
inferredcites →https://doi.org/10.1038/s42254-021-00314-5openalex-snapshot
Physics-informed machine learning
inferredcites →https://doi.org/10.1038/s44172-024-00273-6openalex-snapshot
Synergizing physics and machine learning for advanced battery management
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Lithium ion battery degradation: what you need to know
inferredcites →https://doi.org/10.1109/tpami.2019.2913372openalex-snapshot
Squeeze-and-Excitation Networks
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Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning
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