  Accurately estimating a battery's state of health (SOH) helps prevent
battery-powered applications from failing unexpectedly. With the superiority of
reducing the data requirement of model training for new batteries, transfer
learning (TL) emerges as a promising machine learning approach that applies
knowledge learned from a source battery, which has a large amount of data.
However, the determination of whether the source battery model is reasonable
and which part of information can be transferred for SOH estimation are rarely
discussed, despite these being critical components of a successful TL. To
address these challenges, this paper proposes an interpretable TL-based SOH
estimation method by exploiting the temporal dynamic to assist transfer
learning, which consists of three parts. First, with the help of dynamic time
warping, the temporal data from the discharge time series are synchronized,
yielding the warping path of the cycle-synchronized time series responsible for
capacity degradation over cycles. Second, the canonical variates retrieved from
the spatial path of the cycle-synchronized time series are used for
distribution similarity analysis between the source and target batteries.
Third, when the distribution similarity is within the predefined threshold, a
comprehensive target SOH estimation model is constructed by transferring the
common temporal dynamics from the source SOH estimation model and compensating
the errors with a residual model from the target battery. Through a widely-used
open-source benchmark dataset, the estimation error of the proposed method
evaluated by the root mean squared error is as low as 0.0034 resulting in a 77%
accuracy improvement compared with existing methods.
