  The state of health (SOH) estimation plays an essential role in
battery-powered applications to avoid unexpected breakdowns due to battery
capacity fading. However, few studies have paid attention to the problem of
uneven length of degrading cycles, simply employing manual operation or leaving
to the automatic processing mechanism of advanced machine learning models, like
long short-term memory (LSTM). As a result, this causes information loss and
caps the full capability of the data-driven SOH estimation models. To address
this challenge, this paper proposes an innovative cycle synchronization way to
change the existing coordinate system using dynamic time warping, not only
enabling the equal length inputs of the estimation model but also preserving
all information. By exploiting the time information of the time series, the
proposed method embeds the time index and the original measurements into a
novel indicator to reflect the battery degradation status, which could have the
same length over cycles. Adopting the LSTM as the basic estimation model, the
cycle synchronization-based SOH model could significantly improve the
prediction accuracy by more than 30% compared to the traditional LSTM.
