AI-assisted digital twin modeling technology for lithium-ion batteries

Meiwen Liu, Yaxuan Wang, Junfu Li, Ke Yang, Shilong Guo, Lei Zhao and Zhenbo Wang

Energy, 2025, vol. 331, issue C

Abstract: Accurate assessment of state of health (SOH) for lithium-ion batteries throughout their entire lifecycle and real-time voltage tracking prediction have become urgent requirement to ensure battery system safety and reliability. This paper develops a cloud-edge collaborative digital twin architecture to estimate SOH for LiCoO2 cells and predict the real-time voltage. First, a long short-term memory (LSTM) network model is adopted in the cloud side, where health features are extracted from the current segments of the constant voltage charging phase to estimate the cell's SOH. Subsequently, based on the estimated SOH, the cloud side matches electrochemical parameters and transmits them to the edge-side battery management system (BMS), triggering the rapid simulation by an improved single-particle (SP+) model to obtain preliminary voltage predictions. Considering the impact of cell-to-cell variations and temperature fluctuations on voltage simulation accuracy, an online correction model based on a fully connected neural network (F-CNN) is further constructed to refine the simulation output, ultimately yielding high-accuracy voltage predictions. Experimental results demonstrate that the developed digital twin model enables precise SOH estimation throughout the entire cell lifecycle and achieves high-precision real-time voltage prediction, providing robust support for battery health management and safety applications.

Keywords: State of health assessment; Online update of electrochemical parameters; Online correction model; Cloud-edge collaborative digital twin model (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225026581
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:331:y:2025:i:c:s0360544225026581

DOI: 10.1016/j.energy.2025.137016

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().