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

by   Yan Qin, et al.

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 ongoing cycles.


page 1

page 2

page 3

page 4


A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation

As a significant ingredient regarding health status, data-driven state-o...

Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization

Accurately estimating a battery's state of health (SOH) helps prevent ba...

A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles

Accurate state-of-health (SOH) estimation is critical to guarantee the s...

Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping

The state of health (SOH) estimation plays an essential role in battery-...

Gate Recurrent Unit Network based on Hilbert-Schmidt Independence Criterion for State-of-Health Estimation

State-of-health (SOH) estimation is a key step in ensuring the safe and ...

Lithium-ion Battery Online Knee Onset Detection by Matrix Profile

Lithium-ion batteries (LiBs) degrade slightly until the knee onset, afte...

Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction

Electrochemical batteries are ubiquitous devices in our society. When th...

Please sign up or login with your details

Forgot password? Click here to reset