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

arXiv: https://arxiv.org/abs/2212.04622 · HTML: https://mlsanity.com/paper/2212.04622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Representation Learning via Global Temporal Alignment and Cycle-Consistency](https://mlsanity.com/paper/2105.05217/) (2021)
- [Adaptive Federated Learning and Digital Twin for Industrial Internet of Things](https://mlsanity.com/paper/2010.13058/) (2020)
- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization](https://mlsanity.com/paper/2208.11204/) (2022)
- [A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation](https://mlsanity.com/paper/2209.00190/) (2022)
- [A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering](https://mlsanity.com/paper/2209.06429/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction](https://mlsanity.com/paper/2305.07854/) (2023)
- [Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve](https://mlsanity.com/paper/2409.00141/) (2024)

## Abstract (arXiv)

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.