Abstract
The article describes how to extrapolate a useful time-series of features from a raw dataset of complete charging/discharging cycles of Li-ion batteries. The extrapolation of such time-series based on features is helpful to reduce the size of the LSTM (Long Short-Term Memory) as much as possible, differently from classical approaches with LSTM applied to raw data time-series. After a data pre-processing step, this work implements a features-extraction process that allows selecting the best features to describe the performance degradation of the batteries during the time and to estimate the RUL (Remaining Useful Life) during the battery life.
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Begni, A., Dini, P., Saponara, S. (2023). Design and Test of an LSTM-Based Algorithm for Li-Ion Batteries Remaining Useful Life Estimation. In: Berta, R., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2022. Lecture Notes in Electrical Engineering, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-031-30333-3_51
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DOI: https://doi.org/10.1007/978-3-031-30333-3_51
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