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SeqBattNet: A Discrete-State Physics-Informed Neural Network with Aging Adaptation for Battery Modeling
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/er.5374openalex-snapshot
Lithium‐ion battery SOC / SOH adaptive estimation via simplified single particle model
inferredcites →https://doi.org/10.1007/s11581-024-05933-3openalex-snapshot
An adaptive cubature Kalman filter algorithm based on singular value decomposition for joint estimation of state of charge and state of power for lithium-ion batteries under wide temperature range
inferredcites →https://doi.org/10.1016/j.electacta.2017.10.153openalex-snapshot
A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.erss.2018.10.001openalex-snapshot
Fear and loathing of electric vehicles: The reactionary rhetoric of range anxiety
inferredcites →https://doi.org/10.1016/j.jechem.2023.12.043openalex-snapshot
Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning
inferredcites →https://doi.org/10.1016/j.jechem.2024.08.037openalex-snapshot
Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data
inferredcites →https://doi.org/10.1016/j.jechem.2024.12.068openalex-snapshot
Sensorless battery expansion estimation using electromechanical coupled models and machine learning
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.07.121openalex-snapshot
Remaining discharge-time prediction for batteries using the Lambert function
inferredcites →https://doi.org/10.1016/j.jpowsour.2019.01.012openalex-snapshot
Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.ssi.2004.09.049openalex-snapshot
Modeling of lithium ion cells?A simple equivalent-circuit model approach
inferredcites →https://doi.org/10.1038/s41560-019-0356-8openalex-snapshot
Data-driven prediction of battery cycle life before capacity degradation
inferredcites →https://doi.org/10.1039/d2ee01676aopenalex-snapshot
Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning
inferredcites →https://doi.org/10.1109/access.2019.2925468openalex-snapshot
A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery
What points at this work
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inferred← citeshttps://doi.org/10.1007/s44291-025-00152-0openalex-snapshot
Fleet-level network lifetime prediction for electric vehicle batteries: physics-guided spatio-temporal graph neural networks with interaction effects
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