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• W4387005522 abstract "The battery energy storage system is an essential component in the modern energy system with the development of renewable energy, transportation electrification, and carbon-neutral goals. Battery degradation has been the most challenging issue of energy storage. This work presents a data-driven battery degradation model powered by long short-term memory (LSTM) recurrent neural network (RNN). Utilizing the battery dataset with more than 100 batteries exposed to different operations, the proposed model gives a precise prediction of full-discharge capacity and internal resistance (IR) with the root-mean-square error (RMSE) of 0.008 Ah and 0.00017 Ohm in 100 cycles, respectively. Instead of a single capacity or state of health (SOH) value projection, our model predicts the full-discharge capacity-voltage trajectory of the following cycles, addresses the capacity and energy content in different voltage ranges, and improves the accuracy and applicability of the SOH prognosis in industrial applications." @default.
• W4387005522 created "2023-09-26" @default.
• W4387005522 creator A5044799535 @default.
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• W4387005522 date "2023-07-16" @default.
• W4387005522 modified "2023-09-26" @default.
• W4387005522 title "Battery Health Prognosis: Discharging Capacity Prediction at All Operating Voltage Levels" @default.
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• W4387005522 doi "https://doi.org/10.1109/pesgm52003.2023.10252691" @default.
• W4387005522 hasPublicationYear "2023" @default.
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