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Energy Consumption and Battery Size of Battery Trolley Electric Trucks in Surface Mines

Author

Listed:
  • Haiming Bao

    (School of Mechanical and Mining Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Peter Knights

    (School of Mechanical and Mining Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Mehmet Kizil

    (School of Mechanical and Mining Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Micah Nehring

    (School of Mechanical and Mining Engineering, The University of Queensland, Brisbane 4072, Australia)

Abstract

Mining production, being one of the most energy-intensive industries globally, consumes substantial amounts of fossil fuels and contributes to extensive carbon emissions worldwide. The trend toward electrification and advanced developments in battery technology have shifted attention from diesel power to battery alternatives. These alternatives are appealing, as they contribute to decarbonisation efforts when compared to conventional diesel trucks. This paper presents a comprehensive review of recent technological advancements in powertrains for Mining Haulage Truck (MHT). It also compares these configurations based on mining system-level considerations to assess their future potential. The evaluated configurations include Diesel-Electric Truck (DET), Trolley Assist Truck (TAT), Battery-only Truck (BOT), Battery Trolley with Dynamic charging truck (BT-D), and Battery Trolley with Stationary charging truck (BT-S). According to the analysis, the energy demand for on-board diesel or battery power (excluding trolley power) in these alternative options is as follows: DET—681 kWh, BOT—645 kWh, TAT—511 kWh, BT-S—471 kWh, and BT-D—466 kWh. The paper also illustrates the theory of battery size design based on the current battery technology, battery material selection, battery package design, and battery size selection methods. In the case of tailored battery size selection, BOT, BT-D, and BT-S configurations require LiFePO 4 (LFP) battery masses of 25 tonnes, 18 tonnes, and 18 tonnes, respectively. Based on a techno-economic assessment of battery MHT alternatives with a future perspective, it has been determined that BT-D requires the lowest amount of on-board battery energy. Furthermore, over a span of 20 years, BT-S has demonstrated the lowest on-board battery cost.

Suggested Citation

  • Haiming Bao & Peter Knights & Mehmet Kizil & Micah Nehring, 2024. "Energy Consumption and Battery Size of Battery Trolley Electric Trucks in Surface Mines," Energies, MDPI, vol. 17(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1494-:d:1361184
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    References listed on IDEAS

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    1. Igogo, Tsisilile & Awuah-Offei, Kwame & Newman, Alexandra & Lowder, Travis & Engel-Cox, Jill, 2021. "Integrating renewable energy into mining operations: Opportunities, challenges, and enabling approaches," Applied Energy, Elsevier, vol. 300(C).
    2. Feng, Yanbiao & Dong, Zuomin, 2020. "Integrated design and control optimization of fuel cell hybrid mining truck with minimized lifecycle cost," Applied Energy, Elsevier, vol. 270(C).
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    Citations

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    Cited by:

    1. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
    2. Pruethsan Sutthichaimethee & Grzegorz Mentel & Volodymyr Voloshyn & Halyna Mishchuk & Yuriy Bilan, 2024. "Modeling the Efficiency of Resource Consumption Management in Construction Under Sustainability Policy: Enriching the DSEM-ARIMA Model," Sustainability, MDPI, vol. 16(24), pages 1-17, December.
    3. Chenghao Lyu & Nuo Lei & Chaoyi Chen & Hao Zhang, 2025. "A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles," Energies, MDPI, vol. 18(13), pages 1-20, June.
    4. Bao, Haiming & Knights, Peter & Kizil, Mehmet & Nehring, Micah, 2026. "Comparative assessment of CO2 emissions from mining haulage truck alternatives to achieve 2030 climate targets," Resources Policy, Elsevier, vol. 113(C).
    5. D. Biniaris & G. Xiroudakis & G. Saratsis & G. Exadaktylos & EA Varouchakis, 2025. "Minimization of CO2 Emissions in Openpit Mines by Using Stochastic Simulations," Circular Economy and Sustainability, Springer, vol. 5(3), pages 2317-2345, June.

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