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• W4386948103 startingPage "233582" @default.
• W4386948103 abstract "Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. The framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models." @default.
• W4386948103 created "2023-09-23" @default.
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• W4386948103 date "2023-11-01" @default.
• W4386948103 modified "2023-09-29" @default.
• W4386948103 title "Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning" @default.
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• W4386948103 doi "https://doi.org/10.1016/j.jpowsour.2023.233582" @default.
• W4386948103 hasPublicationYear "2023" @default.
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