Digital-Twin Based SOC and SOH Estimation for Lithium-Ion Batteries
DOI:
https://doi.org/10.32628/CSEIT25116042Keywords:
Electric Vehicles (EVs), Lithium-ion Batteries Digital Twin, State of Health (SOH), State of Charge (SOC), Battery Management System (BMS), Machine Learning, Equivalent Circuit Model (ECM)Abstract
The safe and reliable operation of Electric Vehicles (EVs) depends largely on the performance and health of lithium-ion batteries. This work presents a Digital Twin framework for real-time estimation of two critical battery health parameters: State of Health (SOH) and State of Charge (SOC). A real-world dataset containing over 1,000 charge–discharge cycles is utilized, with features such as voltage, temperature, ohmic resistance (Re), charge transfer resistance (Rct), and capacity. Data preprocessing includes removal of missing values and outliers, along with normalization to enhance model accuracy. SOH is estimated based on capacity degradation and resistance growth, while SOC is derived using Coulomb counting, complemented with voltage–time analysis and Kalman filtering. These estimations are validated through a Simulink-based Electrical Equivalent Circuit Model (ECM) that replicates real-time battery voltage and current characteristics. Furthermore, multiple machine learning models including regression, exponential decay, and random forest are evaluated for SOH prediction using performance metrics such as RMSE and R². The framework is designed with modularity and scalability, enabling real-time integration into intelligent Battery Management Systems (BMS). This ensures adaptability across different EV platforms and operating conditions. The methodology also emphasizes predictive capability, supporting advanced battery diagnostics and prognostics. With the increasing demand for sustainable mobility, reliable and long-lasting energy storage is vital. However, lithium-ion batteries face progressive degradation, affecting both efficiency and safety. The proposed framework addresses this challenge by combining data-driven modeling with Digital Twin technology. Ultimately, this research contributes to the development of scalable, adaptive, and predictive battery health monitoring solutions, thereby enhancing EV safety, efficiency, and lifetime.
Downloads
References
A. Prabhu et al., “Digital Twin for Real-Time Li-Ion Battery State of Health Estimation With Partially Discharged Cycling Data,” IEEE Transactions on Industrial Electronics, 2025.
I. Saba, M. Ullah, and M. Tariq, “Advancing Electric Vehicle Battery Analysis With Digital Twins in Intelligent Transportation Systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 9, pp. 12141–12150, 2024. DOI: https://doi.org/10.1109/TITS.2024.3361807
J. Doe, A. Smith, L. Zhang, and P. Chen, “Transductive Transfer Learning-Assisted Hybrid Deep Learning Model for Accurate State of Charge Estimation of Li-Ion Batteries in Electric Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 10, pp. 13492–13503, 2024. DOI: https://doi.org/10.1109/TITS.2024.3403518
R. Wang et al., “Digital Twin-Based Battery Management Framework: Design and Real-Time Evaluation,” IEEE Internet of Things Journal, 2023.
J. Chen, H. Zhang, and K. Wu, “Digital Twin-Assisted Battery Health Management System Based on Real-Time Data Fusion,” IEEE Trans- actions on Transportation Electrification, vol. 9, no. 1, pp. 129–140, 2023.
X. Liu et al., “Machine Learning-Enabled Digital Twin Framework for Li-ion Battery Life Prediction,” IEEE Transactions on Industrial Informatics, 2023.
Y. Xie et al., “Digital Twin for Electric VehicleBattery Management with Incremental Learning,” IEEE Expert Systems with Applications, 2023.
B. Xia et al., “Verification Platform of SOC Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles,” IEEE Transactions on Transportation Electrification, 2022. DOI: https://doi.org/10.3390/en15093221
K. Zhao, Y. Liu, M. Ming, Y. Zhou, and J. Wu, “Digital Twin-Driven Estimation of State of Charge for Li-ion Battery,” in Proc. IEEE Int. Energy Conf. (ENERGYCON), 2022. DOI: https://doi.org/10.1109/ENERGYCON53164.2022.9830324
K. Sidahmed Alamin et al., “A Machine Learning- Based Digital Twin for Electric Vehicle Battery Modeling,” IEEE Transactions on Trans- portation Electrification, 2022. DOI: https://doi.org/10.1109/COINS54846.2022.9854960
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
