Abstract
The state of health (SOH) estimation of lithium-ion batteries is essential for the safe operation and intelligent maintenance of the battery energy storage system in electric vehicles. Up to now, researches have been done a lot on the SOH estimation of the batteries under standard charging rate, but seldom touched the SOH estimation for fast-charging scenario. This paper proposes a convolutional neural network (CNN) based SOH estimation method, which can extract health features from only 3-minute partial fast-charging segment and then accurately estimate the SOH of the batteries under fast-charging. The proposed method is validated by an open-source dataset, where the root mean square percentage error and mean absolute percentage error are less than 1% and the maximum percentage error is less than 2%. The proposed method may have a great potential in real application for the advantage of high accuracy and small amount of charging data needed.
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