Abstract
Honggang Li, Chaojing Wu, Yongzhi Zhang, Yongjun Pan, Binghe Liu
Abstract
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Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning
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Remaining useful life prediction of Lithium-ion batteries based on health Indicator and gaussian process regression model
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High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with gaussian process regression
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Early prediction of battery lifetime based on graphical features and convolutional neural networks
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Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
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Gaussian process regression for forecasting battery state of health
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Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
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A physics-based aging model for lithium-ion battery with coupled chemical/mechanical degradation mechanisms
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Lithium battery aging model based on Dakin’s degradation approach
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Remaining useful life prediction of Lithium-ion batteries based on wiener process under time-varying temperature condition
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Development of an empirical aging model for Li-ion batteries and application to assess the impact of vehicle-to-grid strategies on battery lifetime
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An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework
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SOH estimation framework for batteriesconsidering label normalization and feature stability under real-world data
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Electrochemical-mechanical understanding of the accelerated degradation of lithium-ion batteries caused by mechanical stress
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Multiaxial failure characterization and short-circuit prediction of large-format prismatic lithium-ion batteries
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Toward the performance evolution of lithium-ion battery upon impact loading
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