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Predicting battery life with early cyclic data by machine learning

https://doi.org/10.1002/est2.98
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Review of energy storage and transportation of energy
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A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime
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A review on the key issues for lithium-ion battery management in electric vehicles
inferredcites →https://doi.org/10.1016/j.jpowsour.2014.02.064openalex-snapshot
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
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Artificial neural network enabled capacitance prediction for carbon-based supercapacitors
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Performance and cost of materials for lithium-based rechargeable automotive batteries
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A survey of decision tree classifier methodology
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Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles
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Battery modeling for energy-aware system design
inferredcites →https://doi.org/10.1149/2.044302jesopenalex-snapshot
Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction
inferredcites →https://doi.org/10.13700/j.bh.1001-5965.2018.0181openalex-snapshot
Life prediction method of lithium battery based on improved relevance vector machine

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Adaptive confidence-calibrated semi-supervised ensemble for early-cycle battery lifetime prediction
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A model-agnostic meta-learning framework for few-shot prediction of battery end-of-life intervals
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Machine learning toward advanced energy storage devices and systems
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Data Mining for Early Cycle Life Prediction in Lithium-Ion Battery Production
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AI to Contribute to the Development of More Efficient and Sustainable Battery Technologies
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