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Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Predicting battery life with early cyclic data by machine learning
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Batteries for sustainability : selected entries from the Encyclopedia of sustainability science and technology
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Neural Network Methods for Natural Language Processing
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Handbuch Lithium-Ionen-Batterien
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Introduction to Machine Learning
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The impact of feature reduction techniques on defect prediction models
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Method for estimating capacity and predicting remaining useful life of lithium-ion battery
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An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks
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The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling
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Remaining useful life estimation – A review on the statistical data driven approaches
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Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach
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A successive approximation method to precisely measure leakage current of the rechargeable Lithium coin battery
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Formation Challenges of Lithium-Ion Battery Manufacturing
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Cycling degradation of an automotive LiFePO4 lithium-ion battery
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Fast formation cycling for lithium ion batteries
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Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging
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Quality Management for Battery Production: A Quality Gate Concept
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Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries
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Current status and challenges for automotive battery production technologies
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A Facile Approach to High Precision Detection of Cell-to-Cell Variation for Li-ion Batteries
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Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
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Remaining Useful Life Prediction of Lithium-Ion Batteries Using Neural Network and Bat-Based Particle Filter
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Introduction to Electrochemical Impedance Spectroscopy as a Measurement Method for the Wetting Degree of Lithium-Ion Cells
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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