In this paper, an ML-enhanced testing procedure for li-ion batteries is proposed, which increases the efficiency of cycle life testing by stopping the cycling of a subset of batteries and predicting their end of lifes instead. Based on aging features and the Kennard-Stone algorithm, a suitable subset of batteries is selected, whose future aging trajectory can be predicted with high accuracy. The…
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