ArXiv Preprint
To meet the fairly high safety and reliability requirements in practice, the
state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a
close relationship with the degradation performance, has been extensively
studied with the widespread applications of various electronics. The
conventional SOH estimation approaches with digital twin are end-of-cycle
estimation that require the completion of a full charge/discharge cycle to
observe the maximum available capacity. However, under dynamic operating
conditions with partially discharged data, it is impossible to sense accurate
real-time SOH estimation for LIBs. To bridge this research gap, we put forward
a digital twin framework to gain the capability of sensing the battery's SOH on
the fly, updating the physical battery model. The proposed digital twin
solution consists of three core components to enable real-time SOH estimation
without requiring a complete discharge. First, to handle the variable training
cycling data, the energy discrepancy-aware cycling synchronization is proposed
to align cycling data with guaranteeing the same data structure. Second, to
explore the temporal importance of different training sampling times, a
time-attention SOH estimation model is developed with data encoding to capture
the degradation behavior over cycles, excluding adverse influences of
unimportant samples. Finally, for online implementation, a similarity
analysis-based data reconstruction has been put forward to provide real-time
SOH estimation without requiring a full discharge cycle. Through a series of
results conducted on a widely used benchmark, the proposed method yields the
real-time SOH estimation with errors less than 1% for most sampling times in
ongoing cycles.
Yan Qin, Anushiya Arunan, Chau Yuen
2022-12-09
