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References from Transferring state of health estimation neural networks for different battery chemistries and charging protocols using renormalization and transfer learning. Local targets link to admitted publications; unresolved targets remain external evidence.
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State estimation for advanced battery management: key challenges and future trends
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A mechanism identification model based state-of-health diagnosis of lithium-ion batteries for energy storage applications
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Machine learning applied to electrified vehicle battery state of charge and state of health estimation: state-of-the-art
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Monitoring battery electrolyte chemistry via in-operando tilted fiber bragg grating sensors
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Deciphering interfacial reactions via optical sensing to tune the interphase chemistry for optimized Na-ion electrolyte formulation
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Optical sensors for operando stress monitoring in lithium-based batteries containing solid-state or liquid electrolytes
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Data-driven prediction of battery cycle life before capacity degradation
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Perspective – combining physics and machine learning to predict battery lifetime
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Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
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Electrochemical impedance spectroscopy based on the state of health estimation for lithium-ion batteries
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A convolutional neural network approach for estimation of Li-ion battery state of health from charge profiles
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External reference
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Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning
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