Digital twin and metaverse-enhanced battery management for electric vehicles

Abstract

The Internet of Things (IoT) and cyber-physical systems (CPS) are driving digital transformation and automation. An essential component of CPS is digital twin (DT) technology, which enables real-time synchronization between physical assets and their virtual counterparts. Battery management systems (BMS) in electric vehicles (EVs) face challenges in handling large volumes of sensor data, often leading to reduced accuracy in battery-state estimation. To address these challenges, DTs have been explored to aid real-time diagnosis and monitoring. One critical step toward the success of DTs is to have practical reference architectures. This paper presents proposes a novel six-layer DT architecture tailored for BMS, extending existing CPS/DT-BMS models by integrating high-fidelity electrochemical modeling, robust nonlinear state estimation, and interactive 3D visualization in a Metaverse environment. The architecture is designed with scalability in mind, supporting deployment on lightweight embedded platforms or via cloud-hosted rendering for resource-limited devices. We validate the approach using MATLAB to develop a thermally coupled SPMe-based DT of a lithium-ion NMC battery, synchronized with a virtual battery model in Unreal Engine for immersive visualization. Experimental results demonstrate accurate state-of-charge estimation (RMSE 0.23%) and low-latency real-time monitoring, highlighting the framework’s potential for deployment in large-scale EV BMS applications.

Keywords

BMS / Digital twin / Metaverse / Battery / MATLAB / Unrealengine / Electric vehicles

CRediT authorship contribution statement

Judith Nkechinyere Njoku: Writing - review & editing, Writing - original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Ebuka Chinaechetam Nkoro: Software, Conceptualization. Robin Matthew Medina: Methodology, Investigation, Conceptualiza-tion. Paul Michael Custodio: Software, Methodology. Cosmas Ifeanyi Nwakanma: Supervision, Methodology, Investigation. Jae-Min Lee: Supervision, Project administration, Funding ac-quisition. Dong-Seong Kim: Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing finan-cial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was partly supported by Innovative Human Re-source Development for Local Intellectualization programthrough the IITP grant funded by the Korea government (MSIT) (IITP-2025-RS-2020-II201612, 25%) and by Priority ResearchCenters Program through the NRF funded by the MEST (2018R1A6A1A03024003, 25%) and by the MSIT, Korea, under the ITRCsupport program (IITP-2025-RS-2024-00438430, 25%), and the Gyeongsangbuk-do RISE (Regional Innovation System & Educa-tion) project (Specialized Industry Scale-up unit, 25%).

References

[1] E. Glaessgen, D. Stargel, The digital twin paradigm for future NASA and US air force vehicles, in: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Struc-tural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, 2012, p. 1818.

[2] C.I. Nwakanma, J.N. Njoku, J.-Y. Jo, C.-H. Lim, D.-S. Kim, "Creativia" metaverse platform for exhibition experience, in: 2022 13th International Conference on Information and Communication Technology Conver-gence, ICTC, 2022, pp. 1789-1793, http://dx.doi.org/10.1109/ICTC55196.2022.9952599.

[3] J.N. Njoku, C.I. Nwakanma, G.C. Amaizu, D.-S. Kim, Prospects and challenges of Metaverse application in data-driven intelligent transportation systems, IET Intell. Transp. Syst. 17 (1) (2023) 1-21, http://dx.doi.org/10.1049/itr2.12252.

[4] H. Dai, B. Jiang, X. Hu, X. Lin, X. Wei, M. Pecht, Advanced battery management strategies for a sustainable energy future: Multilayer de-sign concepts and research trends, Renew. Sustain. Energy Rev. 138 (2021) 110480, http://dx.doi.org/10.1016/j.rser.2020.110480, URL https://www.sciencedirect.com/science/article/pii/S1364032120307668.

[5] Y. Wang, J. Tian, Z. Sun, L. Wang, R. Xu, M. Li, Z. Chen, A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems, Renew. Sustain. Energy Rev. 131 (2020) http://dx.doi.org/10.1016/j.rser.2020.110015.

[6] V. Etacheri, R. Marom, R. Elazari, G. Salitra, D. Aurbach, Challenges in the development of advanced Li-ion batteries: a review, Energy Env. Sci. 4 (2011) 3243-3262, http://dx.doi.org/10.1039/C1EE01598B.

[7] H. Li, M. Bin Kaleem, I.-J. Chiu, D. Gao, J. Peng, A digital twin model for the battery management systems of electric vehicles, in: 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application, HPCC/DSS/SmartCity/DependSys, 2021, pp. 1100-1107, http://dx.doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00171.

[8] W.A. Ali, M. Roccotelli, M.P. Fanti, Digital twin in intelligent transportation systems: a review, in: 2022 8th International Conference on Control, Decision and Information Technologies, Vol. 1, CoDIT, 2022, pp. 576-581, http://dx.doi.org/10.1109/CoDIT55151.2022.9804017.

[9] Y. Wang, R. Xu, C. Zhou, X. Kang, Z. Chen, Digital twin and cloud-side-end collaboration for intelligent battery management system, J. Manuf. Syst. 62 (2022) 124-134, http://dx.doi.org/10.1016/j.jmsy.2021.11.006.

[10] M. Grieves, J. Vickers, Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems, in: Transdisciplinary Perspectives on Complex Systems, Springer, 2017, pp. 85-113.

[11] W. Li, M. Rentemeister, J. Badeda, D. Jöst, D. Schulte, D.U. Sauer, Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation, J. Energy Storage 30 (2020) 101557.

[12] D. Burzyński, L. Kasprzyk, A novel method for the modeling of the state of health of lithium-ion cells using machine learning for practical appli-cations, Knowl.-Based Syst. 219 (2021) 106900, http://dx.doi.org/10.1016/j.knosys.2021.106900, URL https://www.sciencedirect.com/science/article/pii/S0950705121001635.

[13] X. Qu, Y. Song, D. Liu, X. Cui, Y. Peng, Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model, Microelectron. Reliab. 114 (2020) 113857, http://dx.doi.org/10.1016/j.microrel.2020.113857, 31st European Symposium on Reliability of Electron Devices, Failure Physics and Analysis, ESREF 2020, URL https://www.sciencedirect.com/science/article/pii/S0026271420304765.

[14] D. Yang, Y. Cui, Q. Xia, F. Jiang, Y. Ren, B. Sun, Q. Feng, Z. Wang, C. Yang, A digital twin-driven life prediction method of lithium-ion batteries based on adaptive model evolution, Materials 15 (9) (2022) URL https://www.mdpi.com/1996-1944/15/9/3331.

[15] Z. Xu, J. Xu, Z. Guo, H. Wang, Z. Sun, X. Mei, Design and optimization of a novel microchannel battery thermal management system based on digital twin, Energies 15 (4) (2022) URL https://www.mdpi.com/1996-1073/15/4/1421.

[16] S.K. Jagatheesaperumal, M. Rahouti, Building digital twins of cyber physical systems with Metaverse for Industry 5.0 and beyond, IT Prof. 24 (6) (2022) 34-40, http://dx.doi.org/10.1109/MITP.2022.3225064.

[17] Z. Lyu, M. Fridenfalk, Digital twins for building industrial metaverse, J. Adv. Res, (2023) http://dx.doi.org/10.1016/j.jare.2023.11.019, URL https://www.sciencedirect.com/science/article/pii/S2090123223003594.

[18] M. Væ rbak, J.D. Billanes, B.N. Jørgensen, Z. Ma, A digital twin framework for simulating distributed energy resources in distribution grids, Energies 17 (11) (2024) http://dx.doi.org/10.3390/en17112503, URL https://www.mdpi.com/1996-1073/17/11/2503.

[19] J.N. Njoku, C.I. Nwakanma, D.-S. Kim, Explainable data-driven digital twins for predicting battery states in electric vehicles, IEEE Access 12 (2024) 83480-83501, http://dx.doi.org/10.1109/ACCESS.2024.3413075.

[20] K. Zhao, Y. Liu, W. Ming, Y. Zhou, J. Wu, Digital twin-driven estimation of state of charge for Li-ion battery, in: 2022 IEEE 7th International Energy Conference, ENERGYCON, 2022, pp. 1-6, http://dx.doi.org/10.1109/ENERGYCON53164.2022.9830324.

[21] Z. He, Z. Yang, X. Cui, E. Li, A method of state-of-charge estimation for EV power lithium-ion battery using a novel adaptive extended Kalman filter, IEEE Trans. Veh. Technol. 69 (12) (2020) 14618-14630, http://dx.doi.org/10.1109/TVT.2020.3032201.

[22] V. Mali, R. Saxena, K. Kumar, A. Kalam, B. Tripathi, Review on battery thermal management systems for energy-efficient electric vehicles, Re-new. Sustain. Energy Rev. 151 (2021) http://dx.doi.org/10.1016/j.rser.2021.111611.

[23] F.B. Planella, M. Sheikh, W.D. Widanage, Systematic derivation and valida-tion of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods, Electrochim. Acta 388 (2021) 138524, http://dx.doi.org/10.1016/j.electacta.2021.138524.

[24] L. Xia, E. Najafi, H. Bergveld, M. Donkers, A Computationally Efficient Implementation of an Electrochemistry-Based Model for Lithium-Ion Bat-teries**This work has received financial support from the Horizon 2020 programme of the European Union under the grant ‘Integrated Compo-nents for Complexity Control in affordable electrified cars (3Ccar-662192)’ and under the grant ‘Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management (EVERLASTING-713771)’, IFAC-PapersOnLine 50 (1) (2017) 2169-2174, http://dx.doi.org/10.1016/j.ifacol.2017.08.276, 20th IFAC World Congress, URL https://www.sciencedirect.com/science/article/pii/S2405896317305931.

[25] S.J. Moura, F.B. Argomedo, R. Klein, A. Mirtabatabaei, M. Krstic, Battery state estimation for a single particle model with electrolyte dynamics, IEEE Trans. Control Syst. Technol. 25 (2) (2017) 453-468, http://dx.doi.org/10.1109/TCST.2016.2571663.

[26] P. Kemper, D. Kum, Extended single particle model of Li-ion batteries towards high current applications, in: 2013 IEEE Vehicle Power and Propulsion Conference, VPPC, 2013, pp. 1-6, http://dx.doi.org/10.1109/VPPC.2013.6671682.

[27] H.E. Perez, X. Hu, S.J. Moura, Optimal charging of batteries via a single particle model with electrolyte and thermal dynamics, in: 2016 American Control Conference, ACC, 2016, pp. 4000-4005, http://dx.doi.org/10.1109/ACC.2016.7525538.

[28] F. Zhu, J. Fu, A novel state-of-health estimation for lithium-ion battery via unscented Kalman filter and improved unscented particle filter, IEEE Sensors J. 21 (22) (2021) 25449-25456, http://dx.doi.org/10.1109/JSEN.2021.3102990.

[29] V. Sulzer, S.G. Marquis, R. Timms, M. Robinson, S.J. Chapman, Python Battery Mathematical Modelling (PyBaMM), J. Open Res. Softw. 9 (1) (2021) 14, http://dx.doi.org/10.5334/jors.309.

[30] J. Gu, X. Yang, Y. Zhang, L. Xie, L. Wang, W. Zhou, X. Ge, Fuzzy droop control for SOC balance and stability analysis of DC microgrid with distributed energy storage systems, J. Mod. Power Syst. Clean Energy (2024) 1-14, http://dx.doi.org/10.35833/MPCE.2023.000119.

[31] B.R. Beauparlant, M.K. Jouaneh, Simscape multibody modeling of an H-frame type XY positioning system, in: 2021 9th International Conference on Systems and Control, ICSC, 2021, pp. 198-203, http://dx.doi.org/10.1109/ICSC50472.2021.9666571.

[32] S. Butler, T. Oliver, C.J. Headleand, Understanding unreal engine 5 and it’s layers, in: Game Development Patterns with Unreal Engine 5: Build Main-tainable and Scalable Systems with C++ and Blueprint, Packt Publishing, 2024, pp. 3-47, URL https://ieeexplore.ieee.org/document/10460904.

[33] J.N. Njoku, C.I. Nwakanma, J.-M. Lee, D.-S. Kim, Model comparison and selection for battery digital twin development using PyBaMM, in: The 33rd Joint Conference on Communication and Information, JCCI, 2023, pp. 1-2.

[34] D. Widanage, Simscape-Battery-Library, 2023, github, URL https://github.com/WDWidanage/Simscape-Battery-Library.

[35] P. Taheri, M. Yazdanpour, M. Bahrami, Transient three-dimensional thermal model for batteries with thin electrodes, J. Power Sources 243 (2013) 280-289, http://dx.doi.org/10.1016/j.jpowsour.2013.05.175, URL https://www.sciencedirect.com/science/article/pii/S0378775313009737.

[36] C.-H. Chen, F.B. Planella, K. O’Regan, D. Gastol, W.D. Widanage, E. Kendrick, Development of experimental techniques for parameterization of multi-scale lithium-ion battery models, J. Electrochem. Soc. 167 (8) (2020) 080534, http://dx.doi.org/10.1149/1945-7111/ab9050.