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Vol 275
Pages:
56-69
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RUS ENG

The model of wireless charging infrastructure for electric transport of open-pit mining enterprises

Authors:
Irina Yu. Semykina1
Valery M. Zavyalov2
Yaroslava A. Nechiporenko3
Elena N. Taran4
About authors
Date submitted:
2025-04-06
Date accepted:
2025-09-02
Online publication date:
2025-10-13
Date published:
2025-10-31

Abstract

The prospects for implementing battery-powered dump trucks at open-pit mining enterprises are considered. Main attention is on the problem of charging infrastructure for adopting the unmanned production concept. The suggestion is to use wireless charging stations to join charging with particular technological operations, thereby reducing battery capacity and increasing the utilization rate of electric vehicles. To determine effective charging infrastructure solutions, it is necessary to evaluate the interaction between the dump truck and charging stations. The research aims to develop a model reflecting the power flows between the charging infrastructure and the dump truck battery while an operational process is being executed. The model takes into account the work cycle parameters, dump truck parameters with powertrain options providing energy recovery at braking, and charging infrastructure parameters in three options: one stationary charging station located outside the operating routes designed for the simultaneous charging of several dump trucks (option A); stationary charging stations for one dump truck located at loading points (option B); and a dynamic charging station that charges in motion (option C). The method for determining the power of a single wireless charging station is proposed, along with a related method for determining battery capacity. When establishing capacity, the parameters of the charge-discharge cycle and the charging current ratio are considered. The described model is implemented in MATLAB Simulink using m-files for processing satellite data of route parameters from geographic information systems, as well as elements of the Stateflow and Simscape Electrical libraries. The capabilities of the model were demonstrated on the example of the Lebedinsky GOK, with the BelAZ-7558E selected as a battery-powered dump truck. In the example considered, the total capacity of the wireless charging infrastructure for options A, B, and C was 10.6; 6.3, and 13.5 MW, with option B providing the highest value of the battery average state of charge of 0.65 p.u., with the lowest specific power demand per dump truck of 2.4 MW·h. The simulation results allow us to determine various operating factors of the system, evaluate the power compliance of system elements, compare wireless charging infrastructure options, and make informed design decisions.

Область исследования:
Geotechnical Engineering and Engineering Geology
Keywords:
dump truck battery-powered electric vehicles wireless charging station charging infrastructure work cycle power flow computer model
Go to volume 275

Funding

The research was supported by the State assignment of Ministry of Science and Higher Education of the Russian Federation (N 075-03-2024-082-2).

References

  1. Nobahar P., Chaoshui Xu, Dowd P., Shirani Faradonbeh R. Exploring digital twin systems in mining operations: A review. Green and Smart Mining Engineering. 2024. Vol. 1. Iss. 4, p. 474-492. DOI: 10.1016/j.gsme.2024.09.003
  2. Jian-guo Li, Kai Zhan. Intelligent Mining Technology for an Underground Metal Mine Based on Unmanned Equipment. Engineering. 2018. Vol. 4. Iss. 3, p. 381-391. DOI: 10.1016/j.eng.2018.05.013
  3. Zhengguo Hu, Shibin Lin, Xiuhua Long et al. Excavation trajectory planning for unmanned mining electric shovel using B-spline curves and point-by-point incremental strategy under uncertainty. Automation in Construction. 2025. Vol. 174. N 106135. DOI: 10.1016/j.autcon.2025.106135
  4. Dongyang Huo, Jinshi Chen, Tongyang Wang. Chaos-based support vector regression for load power forecasting of excavators. Expert Systems with Applications. 2024. Vol. 246. N 123169. DOI: 10.1016/j.eswa.2024.123169
  5. Yukun Yang, Wei Zhou, Jiskani I.M., Zhiming Wang. Extracting unstructured roads for smart Open-Pit mines based on computer vision: Implications for intelligent mining. Expert Systems with Applications. 2024. Vol. 249. Part C. N 123628. DOI: 10.1016/j.eswa.2024.123628
  6. Lalezar M., Izadi I., Hoseinie S.H., Mohamadrezaie H. A Model Predictive Control Algorithm for Autonomous Mining Dump Trucks. IFAC-PapersOnLine. 2024. Vol. 58. Iss. 22, p. 60-65. DOI: 10.1016/j.ifacol.2024.09.291
  7. Siyu Teng, Luxi Li, Yuchen Li et al. FusionPlanner: A multi-task motion planner for mining trucks via multi-sensor fusion. Mechanical Systems and Signal Processing. 2024. Vol. 208. N 111051. DOI: 10.1016/j.ymssp.2023.111051
  8. Voronov Yu.E., Voronov A.Yu., Dubinkin D.M., Maksimova O.S. Dispatching in truck-shovel systems with unmanned transport at open-pit mines. Ugol. 2023. N 9 (1171), p. 75-83 (in Russian). DOI: 10.18796/0041-5790-2023-9-75-83
  9. Li Zhang, Wenxuan Shan, Bin Zhou, Bin Yu. A dynamic dispatching problem for autonomous mine trucks in open-pit mines considering endogenous congestion. Transportation Research Part C: Emerging Technologies. 2023. Vol. 150. N 104080. DOI: 10.1016/j.trc.2023.104080
  10. Yamini E., Zarnoush M., Jalilvand M. et al. Integration of emerging technologies in next-generation electric vehicles: Evolution, advancements, and regulatory prospects. Results in Engineering. 2025. Vol. 25. N 104082. DOI: 10.1016/j.rineng.2025.104082
  11. Verma S., Sharma A., Tran B., Alahakoon D. A systematic review of digital twins for electric vehicles. Journal of Traffic and Transportation Engineering. 2024. Vol. 11. Iss. 5, p. 815-834. DOI: 10.1016/j.jtte.2024.04.004
  12. Balboa-Espinoza V., Segura-Salazar J., Hunt C. et al. Comparative life cycle assessment of battery-electric and diesel underground mining trucks. Journal of Cleaner Production. 2023. Vol. 425. N 139056. DOI: 10.1016/j.jclepro.2023.139056
  13. Qingsong Tang, Manjiang Hu, Yougang Bian et al. Optimal energy efficiency control framework for distributed drive mining truck power system with hybrid energy storage: A vehicle-cloud integration approach. Applied Energy. 2024. Vol. 374. N 123989. DOI: 10.1016/j.apenergy.2024.123989
  14. Zamyatin I.D. Analysis of the development prospects of the design of the mining dump. Voprosy ustoichivogo razvitiya obshchestva. 2021. N 6, p. 641-651 (in Russian).
  15. Cherepanov V.A., Zhuravlev A.G., Glebov I.A., Chendyrev M.A. Overview of transport with rower supply in focus of mining industry development. Problems of Subsoil Use. 2019. N 1 (20), p. 33-49 (in Russian). DOI: 10.25635/2313-1586.2019.01.033
  16. Hunt J.D., Nascimento A., Wenxuan Tong et al. Perpetual motion electric truck, transporting cargo with zero fuel costs. Journal of Energy Storage. 2023. Vol. 72. Part D. N 108671. DOI: 10.1016/j.est.2023.108671
  17. Khazin M.L. Electric trucks for underground and open pit mining. News of the Ural State Mining University. 2019. Iss. 1 (53), p. 128-135 (in Russian). DOI: 10.21440/2307-2091-2019-1-128-135
  18. Dubinkin D.M., Kartashov A.B., Arutyunyan G.A. et al. Current state of the art and technologies in the field of quarry dump trucks with energy storage devices. Mining Equipment and Electromechanics. 2020. N 6 (152), p. 31-42 (in Russian). DOI: 10.26730/1816-4528-2020-6-31-42
  19. Grachev A.I. Absolutely “green” BELAZ-7558E. Gornaya promyshlennost. 2022. N 2, p. 30-32 (in Russian).
  20. Nguyen T.H., Vasiliev B.Yu. Analysis of autonomous robotic mining machines with autonomous electric propulsion systems. Mining Equipment and Electromechanics. 2022. N 5 (163), p. 59-69 (in Russian). DOI: 10.26730/1816-4528-2022-5-59-69
  21. Sudev V., Sindhu M.R. State-of-the-art and future trends in electric vehicle charging infrastructure: A review. Engineering Science and Technology, an International Journal. 2025. Vol. 62. N 101946. DOI: 10.1016/j.jestch.2025.101946
  22. Revankar S.R., Kalkhambkar V.N. Grid integration of battery swapping station: A review. Journal of Energy Storage. 2021. Vol. 41. N 102937. DOI: 10.1016/j.est.2021.102937
  23. Weipeng Zhan, Zhenpo Wang, Lei Zhang et al. A review of siting, sizing, optimal scheduling, and cost-benefit analysis for battery swapping stations. Energy. 2022. Vol. 258. N 124723. DOI: 10.1016/j.energy.2022.124723
  24. Mahaadevan V.C., Narayanamoorthi R., Logeshwer S.P.P. et al. Integrated design and YOLO based control framework for autonomous EV charging robot platforms. Results in Engineering. 2025. Vol. 26. № 105438. DOI: 10.1016/j.rineng.2025.105438
  25. Santos G.R., Romeral P.A., Zancul E. et al. Exploring electric vehicle robot charging stations: A simulation-based approach for charging capacity improvement. Research in Transportation Business & Management. 2025. Vol. 60. N 101383. DOI: 10.1016/j.rtbm.2025.101383
  26. Hao Chen, Zhongnan Qian, Ruoqi Zhang et al. Modular Four-Channel 50 kW WPT System With Decoupled Coil Design for Fast EV Charging. IEEE Access. 2021. Vol. 9, p. 136083-136093. DOI: 10.1109/ACCESS.2021.3116696
  27. Zavyalov V.M., Semykina I.Yu., Dubkov E.A., Velilyaev A.S. The wireless charging system for mining electric locomotives. Journal of Mining Institute. 2023. Vol. 261, p. 428-442.
  28. Rogge M., Wollny S., Sauer D.U. Fast Charging Battery Buses for the Electrification of Urban Public Transport – A Feasibility Study Focusing on Charging Infrastructure and Energy Storage Requirements. Energies. 2015. Vol. 8. Iss. 5, p. 4587-4606. DOI: 10.3390/en8054587
  29. Rothgang S., Rogge M., Becker J., Sauer D.U. Battery Design for Successful Electrification in Public Transport. Energies. 2015. Vol. 8. Iss. 7, p. 6715-6737. DOI: 10.3390/en8076715
  30. Basso R., Kulcsár B., Egardt B. et al. Energy consumption estimation integrated into the Electric Vehicle Routing Problem. Transportation Research Part D: Transport and Environment. 2019. Vol. 69, p. 141-167. DOI: 10.1016/j.trd.2019.01.006
  31. Yang Xing, Chen Lv, Dongpu Cao, Chao Lu. Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling. Applied Energy. 2020. Vol. 261. N 114471. DOI: 10.1016/j.apenergy.2019.114471
  32. Jia-Hao Syu, Lin J.C.-W., Yu P.S. Multi-head learning models for power consumption prediction of unmanned ground vehicles. Information Fusion. 2025. Vol. 118. N 102895. DOI: 10.1016/j.inffus.2024.102895
  33. Xu Y., Ingelström P., Kersten A. et al. Improving powertrain efficiency through torque modulation techniques in single and dual motor electric vehicles. Transportation Engineering. 2024. Vol. 18. N 100289. DOI: 10.1016/j.treng.2024.100289
  34. Burmistrova O.N., Plastinina E.V., Timokhova O.M. On the determination of dependence of the velocity of the car from the visibility distance on the curve plan. Fundamental Research. 2015. N 2-10, p. 2074-2078 (in Russian).
  35. Bryn M.Ya., Mustafin M.G., Bashirova D.R., Vasilev B.Yu. Investigation of the accuracy of constructing digital elevation models of technogenic massifs based on satellite coordinate determinations. Journal of Mining Institute. 2025. Vol. 271, p. 95-107.
  36. Semykina I.Yu., Zavyalov V.M., Dubkov E.A., Nechiporenko Ya.A. The evaluation of the power of technological connection to electrical systems for the wireless charging infrastructure of battery-powered dump trucks. Problemy i perspektivy razvitiya energetiki, elektrotekhniki i energoeffektivnosti: Materialy VIII Mezhdunarodnoi nauchno-tekhnicheskoi konferentsii, 22 noyabrya 2024. Cheboksary, Rossiya. V 2 chastyakh. Cheboksary: Chuvashskii gosudarstvennyi universitet im. I.N.Ulyanova, 2024. Part 1, p. 265-273 (in Russian).
  37. Pechenko V.V. Single state dynamic battery cell model. Radioengineering. 2015. N 4, p. 58-60 (in Russian).
  38. Hao Mu, Rui Xiong, Fengchun Sun. A Novel Multi-model Probability Based Battery State-of-charge Fusion Estimation Approach. Energy Procedia. 2016. Vol. 88, p. 840-846. DOI: 10.1016/j.egypro.2016.06.061
  39. Rui Xiong, Yongzhi Zhang, Ju Wang et al. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles. IEEE Transactions on Vehicular Technology. 2019. Vol. 68. Iss. 5, p. 4110-4121. DOI: 10.1109/TVT.2018.2864688
  40. De Santis E., Pennazzi V., Luzi M., Rizzi A. Degradation mechanisms and differential curve modeling for non-invasive diagnostics of lithium cells: An overview. Renewable and Sustainable Energy Reviews. 2025. Vol. 211. N 115349. DOI: 10.1016/j.rser.2025.115349
  41. Syrkin I.S., Buzunov N.V., Turgenev I.A. Battery sizes of low-voltage electrical equipment of dump trucks with payload capacity from 218 to 255 tons. Journal of Mining and Geotechnical Engineering. 2022. N 2 (17), p. 53-66 (in Russian). DOI: 10.26730/2618-7434-2022-2-53-66
  42. Golubchik T., Kulikov A. Experimental tests results of lithium-iron-phosphate battery manufactured by LYOTECH under low-temperature conditions. Electronics and Electrical Equipment of Transport. 2021. N 1, p. 17-20 (in Russian).
  43. Gursky A.S. Analysis of parameters of high-voltage batteries of electric buses in order to create algorithms for their general and element-by-element diagnostics using telematics systems. Transport i transportnye sistemy: konstruirovanie, ekspluatatsiya, tekhnologii. Minsk: Belorusskii natsionalnyi tekhnicheskii universitet, 2022. Iss. 4, p. 12-20 (in Russian).
  44. Kuznetsov I.S., Sinoviev V.V., Nikolayev P.I., Starodubov A.N. Simulation modeling computer-based system for optimizing the parameters of open-pit excavator-dump truck complexes. Mining Informational and Analytical Bulletin. 2022. N 6-1, p. 304-316 (in Russian). DOI: 10.25018/0236_1493_2022_61_0_304
  45. Voronov A.Yu. Optimization of operational performance indicators of shovel-truck systems in open-pit mines: Avtoref. dis. … kand. tekhn. nauk. Kemerovo: Kuzbasskii gosudarstvennyi tekhnicheskii universitet imeni T.F.Gorbacheva, 2015, p. 19 (in Russian).

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