More importantly, despite the mature sensory technologies, their integration into battery always casts significant impact on energy density and cycling stability of battery 16, cost of fabrication, and energy consumption for on-site sensing 12, 13, as well as signal drifting and crosstalk due to the variation of local conditions 17, 18, 19.
Since its birth in early 1990s, lithium-ion batteries (LIBs) have revolutionized our daily energy usage over the last few decades, penetrating personal electronics and communications, to large-scale grid-storage, and to the electrification of diversified transportation means, including vehicles, aircrafts, railway, and cargo ships 1, 2, 3.
More importantly, the IISAS demonstrates its capability for practical deployment for battery packs in diversified format, chemistry, and engineering fields. This rarely reported onsite sensing system would render next generation batteries intrinsically intelligent.
The sensor data and electrochemical data of the smart battery were inputted in the format of (batch-size = 8, L = 10, input data’s feature dimensions + 1). These data were processed through one-dimensional CNN to obtain input features for the LSTM.
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