SavedAdd your journal

Journals / International Journal of Automotive Science and Technology / 2026 / Volume: 10 Issue: 3

Conformal Digital-Twin Planning for Pre-Dispatch Electric Vehicle Fleet Routing Under Communication Uncertainty

Abdelkader Kaddour(University of M'Sila)0009-0006-9566-5559
Lamri Sayad(University of M'Sila)0000-0002-0648-1859
Pages
—
Publication Date
2026-10-21
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results remain synthetic pre-dispatch evidence and are not a deployment or safety certificate for fleet routing under communication uncertainty.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Electric-vehicle fleets plan from digital-twin traffic, energy, charging, and station-availability state before dispatch.
📊
Key Findings & Data:Because timestamped calibration rows repeat within synthetic trajectories, we claim empirical coverage only: held-out continuous and joint inclusion are both 92.0%, with trajectory-cluster 95% intervals 89.1–94.7% and 89.1–94.8%.
💡
Conclusion & Impact:Results remain synthetic pre-dispatch evidence and are not a deployment or safety certificate for fleet routing under communication uncertainty.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Age of Information · Charging stations · Conformal prediction

Abstract

Electric-vehicle fleets plan from digital-twin traffic, energy, charging, and station-availability state before dispatch. Communication loss, backend delay, and sensor noise make that state stale or incomplete. We study whether a conservative planning state, rather than update frequency alone, improves electric vehicle routing with time windows (EVRPTW). CP-World maps information age and communication regime to row-level conformal upper bounds. Because timestamped calibration rows repeat within synthetic trajectories, we claim empirical coverage only: held-out continuous and joint inclusion are both 92.0%, with trajectory-cluster 95% intervals 89.1–94.7% and 89.1–94.8%. The primary experiment pairs Raw-6, deterministic Heuristic-6, Pooled-CP-6, and Mondrian-CP-6 on the same six epochs, physical worlds, packet/noise draws, and 6-s end-to-end solver-call deadline. Across 432 cells from 24 unseen 100-customer instances and three seeds, strict release feasibility is 1.2%, 36.1%, 23.6%, and 20.4%, respectively. A six-request feasibility/risk gate is evaluated secondarily. Direct final-world inclusion, common-feasible routing cost, deadline compliance, expanded search, and exact five-customer solutions separate predictive, operational, and solver effects. Together, these endpoints distinguish prediction quality from routing quality and communication-policy behavior under matched conditions. The contribution is a dependence-explicit audit and planning layer, not a new routing metaheuristic. Results remain synthetic pre-dispatch evidence and are not a deployment or safety certificate for fleet routing under communication uncertainty.

🔖

Cite This Article

Kaddour, A., Sayad, L. (2026). Conformal Digital-Twin Planning for Pre-Dispatch Electric Vehicle Fleet Routing Under Communication Uncertainty. International Journal of Automotive Science and Technology, Volume: 10 Issue: 3. https://doi.org/10.30939/ijastech..2012146

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (49)

  1. Schneider M, Stenger A, Goeke D. The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations. Transp Sci. 2014;48(4):500–520. https://doi.org/10.1287/trsc.2013.0490
  2. Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen FJ, Flumerfelt S, Alves A, editors. Transdisciplinary Perspectives on Complex Systems. Cham: Springer; 2017. p. 85–113. https://doi.org/10.1007/978-3-319-38756-7_4
  3. Loubany A, Itani M, Sharafeddine S. From Age of Information to Age of Digital Twin: A Review on Synchronization Metrics for IoT Networks. IEEE Access. 2025;13:131102–131119. https://doi.org/10.1109/ACCESS.2025.3591589
  4. Wang D, Zheng W, Zhou H. Robust Optimization for Electric Vehicle Routing Problem Considering Time Windows Under Energy Consumption Uncertainty. Appl Sci (Basel). 2025;15(2):761. https://doi.org/10.3390/app15020761
  5. Jeong J, Ghaddar B, Zufferey N, Nathwani J. Adaptive Robust Electric Vehicle Routing under Energy Consumption Uncertainty. Transp Res Part C Emerg Technol. 2024;160:104529. https://doi.org/10.1016/j.trc.2024.104529
  6. Yates RD, Sun Y, Brown DR III, Kaul SK, Modiano E, Ulukus S. Age of Information: An Introduction and Survey. IEEE J Sel Areas Commun. 2021;39(5):1183–1210. https://doi.org/10.1109/JSAC.2021.3065072
  7. Guo C, Wang X, Liang L, Li GY. Age of Information, Latency, and Reliability in Intelligent Vehicular Networks. IEEE Netw. 2023;37(6):109–116. https://doi.org/10.1109/MNET.124.2200132
  8. Kahraman S, Özkan M, Sarıçiçek İ, Yazıcı A. Simulation-Based Verification Framework for Outputs of Electric Vehicle Routing Algorithms: A Campus-Scale Case Study. Int J Automot Sci Technol. 2026;10(2):370–385. https://doi.org/10.30939/ijastech.1793294
  9. Goeke D, Schneider M. Routing a Mixed Fleet of Electric and Conventional Vehicles. Eur J Oper Res. 2015;245(1):81–99. https://doi.org/10.1016/j.ejor.2015.01.049
  10. Keskin M, Çatay B. Partial Recharge Strategies for the Electric Vehicle Routing Problem with Time Windows. Transp Res Part C Emerg Technol. 2016;65:111–127. https://doi.org/10.1016/j.trc.2016.01.013
  11. Desaulniers G, Errico F, Irnich S, Schneider M. Exact Algorithms for Electric Vehicle-Routing Problems with Time Windows. Oper Res. 2016;64(6):1388–1405. https://doi.org/10.1287/opre.2016.1535
  12. Qin H, Su X, Ren T, Luo Z. A Review on the Electric Vehicle Routing Problems: Variants and Algorithms. Front Eng Manag. 2021;8(3):370–389. https://doi.org/10.1007/s42524-021-0157-1
  13. Pelletier S, Jabali O, Laporte G. The Electric Vehicle Routing Problem with Energy Consumption Uncertainty. Transp Res Part B Methodol. 2019;126:225–255. https://doi.org/10.1016/j.trb.2019.06.006
  14. Han B, Yan Y, Chi T, Park Y. The Electric Vehicle Routing Problem with Travel Time and Energy Consumption Uncertainty. Transp Res Part E Logist Transp Rev. 2025;202:104211. https://doi.org/10.1016/j.tre.2025.104211
  15. Djebbar AM, Kemmar A. A Hybrid Approach for Solving the Capacitated Vehicle Routing Problem. Int J Automot Sci Technol. 2025;9(2):249–258. https://doi.org/10.30939/ijastech.1663305
  16. Rolich A, Turcanu I, Vinel A, Baiocchi A. Understanding the Impact of Persistence and Propagation on the Age of Information of Broadcast Traffic in 5G NR-V2X Sidelink Communications. Comput Netw. 2024;248:110503. https://doi.org/10.1016/j.comnet.2024.110503
  17. Zhang C, Ji M, Wu Q, Fan P, Fan Q. Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels. Sensors (Basel). 2024;24(24):7888. https://doi.org/10.3390/s24247888
  18. Moradi-Pari E, Tian D, Bahramgiri M, Rajab S, Bai S. DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies. IEEE Trans Intell Transp Syst. 2023;24(5):4889–4903. https://doi.org/10.1109/TITS.2023.3247339
  19. Ali WA, Fanti MP, Roccotelli M, Ranieri L. A Review of Digital Twin Technology for Electric and Autonomous Vehicles. Appl Sci (Basel). 2023;13(10):5871. https://doi.org/10.3390/app13105871
  20. Naseri F, Gil S, Barbu C, Cetkin E, Yarimca G, Jensen AC, et al. Digital Twin of Electric Vehicle Battery Systems: Comprehensive Review of the Use Cases, Requirements, and Platforms. Renew Sustain Energy Rev. 2023;179:113280. https://doi.org/10.1016/j.rser.2023.113280
  21. Wang K, Li Z, Nonomura K, Yu T, Sakaguchi K, Hashash O, et al. Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept. IEEE Trans Intell Veh. 2024;9(3):4348–4361. https://doi.org/10.1109/TIV.2024.3368109
  22. Kaarlela T, Alekseev S, Maljamäki P, Ikäheimo E, Kurvinen E. Digital Twin Framework for Electric Truck Route and Infrastructure Planning in the Nordic Region. Transp Eng. 2026;23:100426. https://doi.org/10.1016/j.treng.2026.100426
  23. Kew SYN. Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. Int J Automot Sci Technol. 2026;10(1):1–25. https://doi.org/10.30939/ijastech.1814700
  24. Simon N, Peszleg R. Comparative Analysis of Electric Vehicle Energy Consumption in Urban and Highway Environments Using CAN-Based Data Collection. Engineering Perspective. 2025;5(1st Future of Vehicles Conf.):8–13. https://doi.org/10.64808/engineeringperspective.1795030
  25. Ekici YE. A Decision Tree-Based Dynamic Maintenance Interval Model Incorporating Driver Behavior and Traffic Congestion: An Application to an Electric Bus Fleet. Engineering Perspective. 2026;6(3):415–427. https://doi.org/10.64808/engineeringperspective.1904906
  26. Yang X, Zheng J, Luan TH, Li R, Su Z, Dong M. Data Synchronization for Vehicular Digital Twin Network. In: GLOBECOM 2022 - 2022 IEEE Global Communications Conference; 2022 Dec 4–8; Rio de Janeiro, Brazil. IEEE; 2022. p. 5795–5800. https://doi.org/10.1109/GLOBECOM48099.2022.10000831
  27. Zheng J, Luan TH, Zhang Y, Li R, Hui Y, Gao L, et al. Data Synchronization in Vehicular Digital Twin Network: A Game Theoretic Approach. IEEE Trans Wirel Commun. 2023;22(11):7635–7647. https://doi.org/10.1109/TWC.2023.3254158
  28. Li Y, Hui Y, Tian M, Cheng N, Sun R, Luan TH. On-Demand Data Synchronization for Differentiated Digital Twins in Vehicular Networks. In: 2024 IEEE/CIC International Conference on Communications in China (ICCC); 2024 Aug 7–9; Hangzhou, China. IEEE; 2024. p. 150–154. https://doi.org/10.1109/ICCC62479.2024.10681749
  29. Li K, Zhang X, Li J, Huang H, Luo S, Xing H. AoI-Error-Aware Data Synchronization for Vehicular Digital Twin. In: GLOBECOM 2025 - 2025 IEEE Global Communications Conference; 2025 Dec 8–12; Taipei, Taiwan. IEEE; 2025. p. 2565–2570. https://doi.org/10.1109/GLOBECOM59602.2025.11432664
  30. Bi X, Li M, Sultana A, Zhao L. Adaptive Information Synchronization and Prediction for Digital Twin-Enabled Autonomous Driving. IEEE Trans Cogn Commun Netw. 2026;12:3802–3814. https://doi.org/10.1109/TCCN.2025.3626412
  31. Chen X, Bi Y, Xing H, Zheng D, Marina MK. Model Migration in Digital Twin-Empowered Vehicular Edge Computing with AoI-Aware Decentralized Bilevel Learning. IEEE Trans Mob Comput. 2026;25(3):3953–3968. https://doi.org/10.1109/TMC.2025.3621052
  32. Cakir LV, Thomson CJ, Özdem M, Canberk B, Nguyen VL, Duong TQ. Intelligent Digital Twin Communication Framework for Addressing Accuracy and Timeliness Tradeoff in Resource-Constrained Networks. IEEE Trans Cogn Commun Netw. 2025;11(3):1954–1965. https://doi.org/10.1109/TCCN.2024.3469234
  33. Lu B, Huang X, Wu Y, Qian L, Niyato D, Xu C. Cooperative Perception Aided Digital Twin Model Update and Migration in Mixed Vehicular Networks. IEEE Trans Intell Transp Syst. 2025;26(2):2293–2308. https://doi.org/10.1109/TITS.2024.3496121
  34. Kim KS, Kim JW, Lee HS. Prediction-Based Scheduling for Digital Twin Synchronization With AoI and Spatial Urgency. IEEE Trans Ind Inform. 2026;22(10):9280–9291. https://doi.org/10.1109/TII.2026.3698880
  35. Wang W, Cheng N, Li M, Yang T, Zhou C, Li C, et al. Value Matters: A Novel Value of Information-Based Resource Scheduling Method for CAVs. IEEE Trans Veh Technol. 2024;73(6):8720–8735. https://doi.org/10.1109/TVT.2024.3355119
  36. Vovk V, Gammerman A, Shafer G. Algorithmic Learning in a Random World. New York: Springer; 2005. https://doi.org/10.1007/b106715
  37. Romano Y, Patterson E, Candès EJ. Conformalized Quantile Regression. In: Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Curran Associates, Inc.; 2019. p. 3543–3553. https://proceedings.neurips.cc/paper/2019/hash/5103c3584b063c431bd1268e9b5e76fb-Abstract.html
  38. Angelopoulos AN, Bates S. Conformal Prediction: A Gentle Introduction. Found Trends Mach Learn. 2023;16(4):494–591. https://doi.org/10.1561/2200000101
  39. Lindemann L, Cleaveland M, Shim G, Pappas GJ. Safe Planning in Dynamic Environments Using Conformal Prediction. IEEE Robot Autom Lett. 2023;8(8):5116–5123. https://doi.org/10.1109/LRA.2023.3292071
  40. Gibbs I, Candès EJ. Adaptive Conformal Inference Under Distribution Shift. In: Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, Inc.; 2021. p. 1660–1672. https://proceedings.neurips.cc/paper/2021/hash/0d441de75945e5acbc865406fc9a2559-Abstract.html
  41. Xie Y, Li S, Lu L, Pan Y, Iida F. A Digital Twin-Based Approach for Dynamic Traffic-Aware Routing and Charging of Electric Vehicles. Expert Syst Appl. 2026;312:131380. https://doi.org/10.1016/j.eswa.2026.131380
  42. Nonomura K, Wang K, Li Z, Yu T, Sakaguchi K. Digital Twin-Empowered Cooperative Autonomous Car-Sharing Services: Proof-of-Concept. In: 2025 IEEE Intelligent Vehicles Symposium (IV); 2025 Jun 22–25; Cluj-Napoca, Romania. IEEE; 2025. p. 2402–2408. https://doi.org/10.1109/IV64158.2025.11097446
  43. Ropke S, Pisinger D. An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows. Transp Sci. 2006;40(4):455–472. https://doi.org/10.1287/trsc.1050.0135
  44. Alvarez Lopez P, Behrisch M, Bieker-Walz L, Erdmann J, Flötteröd YP, Hilbrich R, et al. Microscopic Traffic Simulation Using SUMO. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC); 2018 Nov 4–7; Maui, HI, USA. IEEE; 2018. p. 2575–2582. https://doi.org/10.1109/ITSC.2018.8569938
  45. Haklay M, Weber P. OpenStreetMap: User-Generated Street Maps. IEEE Pervasive Comput. 2008;7(4):12–18. https://doi.org/10.1109/MPRV.2008.80
  46. Mo R, Wu B, Tan Z, Qiu H. SEE-V2X: C-V2X Direct Communication Dataset: An Application-Centric Approach. In: Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems; 2025 May 6–9; Irvine, CA, USA. ACM; 2025. p. 305–311. https://doi.org/10.1145/3715014.3722077
  47. Kahraman S, Özkan M, Sarıçiçek İ, Yazıcı A. Simulation-Based Verification Framework for Outputs of Electric Vehicle Routing Algorithms: A Campus-Scale Case Study. Int J Automot Sci Technol. 2026;10(2):370–385. https://doi.org/10.30939/ijastech.1793294
  48. Djebbar AM, Kemmar A. A Hybrid Approach for Solving the Capacitated Vehicle Routing Problem. Int J Automot Sci Technol. 2025;9(2):249–258. https://doi.org/10.30939/ijastech.1663305
  49. Kew SYN. Predictive Battery Load Forecasting in Electric Fleets Hybridized with Methanol-Derived Hydrogen Fuel Cells. Int J Automot Sci Technol. 2026;10(1):1–25. https://doi.org/10.30939/ijastech.1814700