STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Automobile Engineering and Applications Review Article

Recent Advances and Future Prospects in Digital Twin Technology for Battery Management Systems of Electric Vehicles

  1. Bhushan Chavan Department of Mechanical Engineering, Pacific Academy of Higher Education and Research University, Udaipur
  2. Manish Pokharna Department of Mechanical Engineering, Pacific Academy of Higher Education and Research University, Udaipur
  3. Sameer Nanivadekar Department of Information Technology, A.P. Shah Institute of Technology, Thane
  4. Amol Shinde Department of Mechanical Engineering, A.P. Shah Institute of Technology, Thane

Abstract

Digital twin technology in battery management systems (BMS) for electric cars (EVs) represents a major development in the automotive industry. Digital twins provide predictive maintenance, modelling, and real-time monitoring by creating virtual copies of real-time monitoring, actual battery systems. This paper describes the functional components, architecture, and design of Digital Twin technology along with how it may be included into BMS. Emphasizing how consistent data flow from sensors improves battery safety and performance, it looks at the benefits of merging and gathering data in real time. The paper emphasizes the use of modern machine learning techniques for predictive maintenance, which may identify any issues early on and resolve them to extend battery life and save money. The report also addresses the prerequisites for high computational needs, robust data integration systems, and data security concerns. By means of case studies and practical results, the paper demonstrates how well Digital Twin technology addresses heat control, battery management, and guarantees of effective energy consumption. The results show that digital twin technology has great power to revolutionize EV BMS and inspire innovation in the electric car sector by means of efficiency. This study reviews existing research status as well as future perspectives for the integration of Digital Twin in BMS for EVs.

Keywords

References (148)

  1. M.R. Abayadeera, “Digital Twin Technology: A Comprehensive Review,” International Journal of
  2. Scientific Research and Engineering Trends, vol. 10, no. 4, pp. 1485–1504, Jul. 2024, doi:
  3. 61137/ijsret.vol.10.issue4.199.
  4. C.T.N.M. Branco and J. M. Fontanela, “A design methodology to employ digital twins for remaining
  5. useful lifetime prediction in electric vehicle batteries,” Jan. 2024. doi:10.4271/2023-36-0132.
  6. M. Ibrahim, V. Rjabtšikov, and R. Gilbert, “Overview of Digital Twin Platforms for EV
  7. Applications,” Sensors, vol. 23, no. 3, p. 1414, Jan. 2023, doi:10.3390/s23031414.
  8. Y. Zou, X. Hu, H. Ma, and S. E. Li, “Combined State of Charge and State of Health estimation over
  9. lithium-ion battery cell cycle lifespan for electric vehicles,” J Power Sources, vol. 273, pp. 793–803, Jan. 2015, doi:10.1016/j.jpowsour.2014.09.146.
  10. J. Nkechinyere Njoku, C. Ifeanyi Nwakanma, and D.-S. Kim, “Explainable Data-Driven Digital
  11. Twins for Predicting Battery States in Electric Vehicles,” IEEE Access, vol. 12, pp. 83480–83501,
  12. 2024, doi:10.1109/ACCESS.2024.3413075.
  13. A. Kulkarni et al., “Li-ion Battery Digital Twin Based on Online Impedance Estimation,” in 2023
  14. IEEE 17th International Conference on Compatibility, Power Electronics and Power Engineering
  15. (CPE-POWERENG), IEEE, Jun. 2023, pp. 1–6. doi:10.1109/CPE-
  16. POWERENG58103.2023.10227419.
  17. N. Kharlamova and S. Hashemi, “Evaluating Machine-Learning-Based Methods for Modeling a
  18. Digital Twin of Battery Systems Providing Frequency Regulation,” IEEE Syst J, vol. 17, no. 2, pp.
  19. 2698–2708, Jun. 2023, doi:10.1109/JSYST.2023.3238287.
  20. X. Hu, L. Xu, X. Lin, and M. Pecht, “Battery Lifetime Prognostics,” Joule, vol. 4, no. 2, pp. 310–346,
  21. Feb. 2020, doi:10.1016/j.joule.2019.11.018.
  22. W. A. Ali, M. Roccotelli, and M. P. Fanti, “Digital Twin in Intelligent Transportation Systems: a
  23. Review,” in 2022 8th International Conference on Control, Decision and Information Technologies
  24. (CoDIT), IEEE, May 2022, pp. 576–581. doi:10.1109/CoDIT55151.2022.9804017.
  25. V. Bugueno, K. A. Barbosa, S. Rajendran, and M. Diaz, “An Overview of Digital Twins Methods
  26. Applied to Lithium-Ion Batteries,” in 2022 IEEE International Conference on Automation/XXV
  27. Congress of the Chilean Association of Automatic Control (ICA-ACCA), IEEE, Oct. 2022, pp. 1–7.
  28. doi:10.1109/ICA-ACCA56767.2022.10006169.
  29. J. Xie, R. Yang, S.-Y. R. Hui, and H. D. Nguyen, “Dual Digital Twin: Cloud–edge collaboration with
  30. Lyapunov-based incremental learning in EV batteries,” Appl Energy, vol. 355, p. 122237, Feb. 2024,
  31. doi:10.1016/j.apenergy.2023.122237.
  32. P. H. K. Utama, I. N. Haq, E. Leksono, M. I. Juristian, G. A. Alim, and J. Pradipta, “Development of
  33. Digital Twin Platform for Electric Vehicle Battery System,” International Journal of Sustainable
  34. Transportation Technology, vol. 6, no. 1, pp. 7–11, Apr. 2023, doi:10.31427/IJSTT.2023.6.1.2.
  35. M. R. Abayadeera and G. U. Ganegoda, “Digital Twin Technology: A Comprehensive Review,”
  36. International Journal of Innovative Science and Research Technology (IJISRT), pp. 640–661, Jun.
  37. 2024, doi:10.38124/ijisrt/IJISRT24JUN425.
  38. I. Saba, M. Ullah, and M. Tariq, “Advancing Electric Vehicle Battery Analysis With Digital Twins in
  39. Intelligent Transportation Systems,” IEEE Transactions on Intelligent Transportation Systems, vol.
  40. 25, no. 9, pp. 12141–12150, Sep. 2024, doi:10.1109/TITS.2024.3361807.
  41. P. K. Rajesh, T. Soundarya, and K. V. Jithin, “Driving sustainability - The role of digital twin in
  42. enhancing battery performance for electric vehicles,” J Power Sources, vol. 604, p. 234464, Jun.
  43. 2024, doi:10.1016/j.jpowsour.2024.234464.
  44. G. Bhatti, H. Mohan, and R. Raja Singh, “Towards the future of smart electric vehicles: Digital twin
  45. technology,” Renewable and Sustainable Energy Reviews, vol. 141, p. 110801, May 2021, doi:
  46. 1016/j.rser.2021.110801.
  47. W. Wang, J. Wang, J. Tian, J. Lu, and R. Xiong, “Application of Digital Twin in Smart Battery
  48. Management Systems,” Chinese Journal of Mechanical Engineering, vol. 34, no. 1, p. 57, Dec. 2021,
  49. doi:10.1186/s10033-021-00577-0.
  50. A. Mahmoudzadeh Andwari, A. Pesiridis, S. Rajoo, R. Martinez-Botas, and V. Esfahanian, “A
  51. review of Battery Electric Vehicle technology and readiness levels,” Renewable and Sustainable
  52. Energy Reviews, vol. 78, pp. 414–430, Oct. 2017, doi:10.1016/j.rser.2017.03.138.
  53. Y. Li et al., “Data-driven health estimation and lifetime prediction of lithium-ion batteries: A
  54. review,” Renewable and Sustainable Energy Reviews, vol. 113, p. 109254, Oct. 2019, doi:
  55. 1016/j.rser.2019.109254.
  56. H. de Carvalho Pinheiro, “PerfECT Design Tool: Electric Vehicle Modelling and Experimental
  57. Validation,” World Electric Vehicle Journal, vol. 14, no. 12, p. 337, Dec. 2023, doi:
  58. 3390/wevj14120337.
  59. F. Naseri et al., “Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms,” Renewable and Sustainable Energy Reviews, vol. 179, p.
  60. 113280, Jun. 2023, doi:10.1016/j.rser.2023.113280.
  61. Y. Zhang, R. Xiong, H. He, and M. G. Pecht, “Long Short-Term Memory Recurrent Neural Network
  62. for Remaining Useful Life Prediction of Lithium-Ion Batteries,” IEEE Trans Veh Technol, vol. 67,
  63. no. 7, pp. 5695–5705, Jul. 2018, doi:10.1109/TVT.2018.2805189.
  64. S. M. Rezvanizaniani, Z. Liu, Y. Chen, and J. Lee, “Review and recent advances in battery health
  65. monitoring and prognostics technologies for electric vehicle (EV) safety and mobility,” J Power
  66. Sources, vol. 256, pp. 110–124, Jun. 2014, doi:10.1016/j.jpowsour.2014.01.085.
  67. A. Biswas and A. Emadi, “Energy Management Systems for Electrified Powertrains: State-of-the-Art
  68. Review and Future Trends,” IEEE Trans Veh Technol, vol. 68, no. 7, pp. 6453–6467, Jul. 2019, doi:
  69. 1109/TVT.2019.2914457.
  70. S. Singh, M. Weeber, and K. P. Birke, “Implementation of Battery Digital Twin: Approach,
  71. Functionalities and Benefits,” Batteries, vol. 7, no. 4, p. 78, Nov. 2021, doi:
  72. 3390/batteries7040078.
  73. R. Gilbert Zequera, A. Rassõlkin, T. Vaimann, and A. Kallaste, “Overview of battery energy storage
  74. systems readiness for digital twin of electric vehicles,” IET Smart Grid, vol. 6, no. 1, pp. 5–16, Feb.
  75. 2023, doi:10.1049/stg2.12101.
  76. G. Pasolini et al., “Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies,” Sensors,
  77. vol. 18, no. 4, p. 1118, Apr. 2018, doi:10.3390/s18041118.
  78. M. Tesar, K. Berthold, J.-P. Gruhler, and P. Gratzfeld, “Design Methodology for the Electrification
  79. of Urban Bus Lines with Battery Electric Buses,” Transportation Research Procedia, vol. 48, pp.
  80. 2038–2055, 2020, doi:10.1016/j.trpro.2020.08.264.
  81. A. Shekhar, V. Prasanth, P. Bauer, and M. Bolech, “Economic Viability Study of an On-Road
  82. Wireless Charging System with a Generic Driving Range Estimation Method,” Energies (Basel), vol.
  83. 9, no. 2, p. 76, Jan. 2016, doi:10.3390/en9020076.
  84. G. Li et al., “Longitudinal Research on Aging Drivers (LongROAD): study design and methods,” Inj
  85. Epidemiol, vol. 4, no. 1, p. 22, Dec. 2017, doi:10.1186/s40621-017-0121-z.
  86. Vandana, A. Garg, and B. K. Panigrahi, “Multi‐dimensional digital twin of energy
  87. storage system for electric vehicles: A brief review,” Energy Storage, vol. 3, no. 6, Dec. 2021, doi:
  88. 1002/est2.242.
  89. N. Bazmohammadi et al., “Microgrid Digital Twins: Concepts, Applications, and Future Trends,”
  90. IEEE Access, vol. 10, pp. 2284–2302, 2022, doi:10.1109/ACCESS.2021.3138990.
  91. F. Chen and G. Fang, “Harnessing digital twin and IoT for real-time monitoring, diagnostics, and
  92. error correction in domestic solar energy storage,” Energy Reports, vol. 11, pp. 3614–3623, Jun.
  93. 2024, doi:10.1016/j.egyr.2024.03.024.
  94. G. He, Q. Chen, C. Kang, P. Pinson, and Q. Xia, “Optimal Bidding Strategy of Battery Storage in
  95. Power Markets Considering Performance-Based Regulation and Battery Cycle Life,” IEEE Trans
  96. Smart Grid, vol. 7, no. 5, pp. 2359–2367, Sep. 2016, doi:10.1109/TSG.2015.2424314.
  97. Y. Chon, G. Lee, R. Ha, and H. Cha, “Crowdsensing-based smartphone use guide for battery life
  98. extension,” in Proceedings of the 2016 ACM International Joint Conference on Pervasive and
  99. Ubiquitous Computing, New York, NY, USA: ACM, Sep. 2016, pp. 958–969. doi:
  100. 1145/2971648.2971728.
  101. “Algorithms for Advanced Battery-Management Systems,” IEEE Control Syst, vol. 30, no. 3, pp.
  102. 49–68, Jun. 2010, doi:10.1109/MCS.2010.936293.
  103. C. C. Chan, A. Bouscayrol, and K. Chen, “Electric, Hybrid, and Fuel-Cell Vehicles: Architectures
  104. and Modeling,” IEEE Trans Veh Technol, vol. 59, no. 2, pp. 589–598, Feb. 2010, doi:
  105. 1109/TVT.2009.2033605.
  106. G. Bhatti, H. Mohan, and R. Raja Singh, “Towards the future of smart electric vehicles: Digital twin
  107. technology,” Renewable and Sustainable Energy Reviews, vol. 141, p. 110801, May 2021, doi:
  108. 1016/j.rser.2021.110801.
  109. M. Jafari, A. Kavousi-Fard, T. Chen, and M. Karimi, “A Review on Digital Twin Technology in
  110. Smart Grid, Transportation System and Smart City: Challenges and Future,” IEEE Access, vol. 11,
  111. pp. 17471–17484, 2023, doi:10.1109/ACCESS.2023.3241588.
  112. M. Pooyandeh and I. Sohn, “Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-
  113. Empowered Digital Twin Approach,” Mathematics, vol. 11, no. 23, p. 4865, Dec. 2023, doi:
  114. 3390/math11234865.
  115. M. Bin Kaleem, W. He, and H. Li, “Machine learning driven digital twin model of Li-ion batteries in
  116. electric vehicles: a review,” AI and Autonomous Systems, 2023, doi:10.55092/aias20230003.
  117. A. P. Renold and N. S. Kathayat, “Comprehensive Review of Machine Learning, Deep Learning, and
  118. Digital Twin Data-Driven Approaches in Battery Health Prediction of Electric Vehicles,” IEEE
  119. Access, vol. 12, pp. 43984–43999, 2024, doi:10.1109/ACCESS.2024.3380452.
  120. N. D. K. M. Eaty and P. Bagade, “Digital twin for electric vehicle battery management with
  121. incremental learning,” Expert Syst Appl, vol. 229, p. 120444, Nov. 2023, doi:
  122. 1016/j.eswa.2023.120444.
  123. E. Din, C. Schaef, K. Moffat, and J. T. Stauth, “A Scalable Active Battery Management System With
  124. Embedded Real-Time Electrochemical Impedance Spectroscopy,” IEEE Trans Power Electron, vol.
  125. 32, no. 7, pp. 5688–5698, Jul. 2017, doi:10.1109/TPEL.2016.2607519.
  126. B. Zhao, X. Zhang, J. Chen, C. Wang, and L. Guo, “Operation Optimization of Standalone
  127. Microgrids Considering Lifetime Characteristics of Battery Energy Storage System,” IEEE Trans
  128. Sustain Energy, vol. 4, no. 4, pp. 934–943, Oct. 2013, doi:10.1109/TSTE.2013.2248400.
  129. E. Hossain, D. Murtaugh, J. Mody, H. M. R. Faruque, Md. S. Haque Sunny, and N. Mohammad, “A
  130. Comprehensive Review on Second-Life Batteries: Current State, Manufacturing Considerations,
  131. Applications, Impacts, Barriers & Potential Solutions, Business Strategies, and Policies,” IEEE
  132. Access, vol. 7, pp. 73215–73252, 2019, doi:10.1109/ACCESS.2019.2917859.
  133. X. Luo, J. Wang, M. Dooner, and J. Clarke, “Overview of current development in electrical energy
  134. storage technologies and the application potential in power system operation,” Appl Energy, vol. 137,
  135. pp. 511–536, Jan. 2015, doi:10.1016/j.apenergy.2014.09.081.
  136. T. Elwert et al., “Current Developments and Challenges in the Recycling of Key Components of
  137. (Hybrid) Electric Vehicles,” Recycling, vol. 1, no. 1, pp. 25–60, Oct. 2015, doi:
  138. 3390/recycling1010025.
  139. J. A. Sanguesa, V. Torres-Sanz, P. Garrido, F. J. Martinez, and J. M. Marquez-Barja, “A Review on
  140. Electric Vehicles: Technologies and Challenges,” Smart Cities, vol. 4, no. 1, pp. 372–404, Mar. 2021,
  141. doi:10.3390/smartcities4010022.
  142. X. Han et al., “A review on the key issues of the lithium ion battery degradation among the whole life
  143. cycle,” eTransportation, vol. 1, p. 100005, Aug. 2019, doi:10.1016/j.etran.2019.100005.
  144. F. Un-Noor, S. Padmanaban, L. Mihet-Popa, M. Mollah, and E. Hossain, “A Comprehensive Study
  145. of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction
  146. of Development,” Energies (Basel), vol. 10, no. 8, p. 1217, Aug. 2017, doi:10.3390/en10081217.
  147. M. R. Palacín and A. de Guibert, “Why do batteries fail?,” Science (1979), vol. 351, no. 6273, Feb.
  148. 2016, doi:10.1126/science.1253292.
Support