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To meet the fairly high safety and reliability requirements in practice, thestate of health (SOH) estimation of Lithium-ion batteries (LIBs), which has aclose relationship with the degradation performance, has been extensivelystudied with the widespread applications of various electronics. Theconventional SOH estimation approaches with digital twin are end-of-cycleestimation that require the completion of a full charge/discharge cycle toobserve the maximum available capacity. However, under dynamic operatingconditions with partially discharged data, it is impossible to sense accuratereal-time SOH estimation for LIBs. To bridge this research gap, we put forwarda digital twin framework to gain the capability of sensing the battery's SOH onthe fly, updating the physical battery model. The proposed digital twinsolution consists of three core components to enable real-time SOH estimationwithout requiring a complete discharge. First, to handle the variable trainingcycling data, the energy discrepancy-aware cycling synchronization is proposedto align cycling data with guaranteeing the same data structure. Second, toexplore the temporal importance of different training sampling times, atime-attention SOH estimation model is developed with data encoding to capturethe degradation behavior over cycles, excluding adverse influences ofunimportant samples. Finally, for online implementation, a similarityanalysis-based data reconstruction has been put forward to provide real-timeSOH estimation without requiring a full discharge cycle. Through a series ofresults conducted on a widely used benchmark, the proposed method yields thereal-time SOH estimation with errors less than 1% for most sampling times inongoing cycles.

Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data