An important conclusion that I make for myself from the "Closed-loop optimization of fast-charging protocols for batteries with machine learning” paper is that the data from cell experiments is often “tainted” by something accidental:

• The main proposition of the paper, that optimal fast-charging protocols are flat rather than decreasing by the charging rate, might be due to the fact that the cells didn’t have time to cool down between the charge-discharge cycles during the experiment.
• The model for early estimation of a cell’s life is biased probably by the shelf life of the cells on which this model was trained.
• Cylindrical cell discharge capacity can increase over the first few cycles not due to some fundamental electrochemical effects, but probably because of the geometry of cylindrical cells (the anode and cathode stripes have different lengths).

Cell may appear to recover capacity during calendar ageing due to initial decrease of impedance not due to fundamental increase of capacity, but because the discharge protocol with a fixed cutoff cell voltage stops earlier when the Cell internal resistance is higher.

Rise of cell internal resistance can explain the capacity fade "knee".

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"If not properly controlled, geometrical and chemical features of the reference electrodes can have a significant influence on the measured response."

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