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Improving Energy Efficiency and Traction Stability in Distributed Electric Wheel Loaders with Preferred-Motor and Load-Ratio Strategies

Author

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  • Wenlong Shen

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
    Department of Automobile Engineering, Jiangsu Vocational College of Electronics and Information, Huai’an 223003, China)

  • Shenrui Han

    (College of Ecology and Environment, Hainan Tropical Ocean University, Sanya 572022, China)

  • Xiaotao Fei

    (Department of Automobile Engineering, Jiangsu Vocational College of Electronics and Information, Huai’an 223003, China)

  • Yuan Gao

    (School of Instrument and Electronics, North University of China, Taiyuan 030051, China)

  • Changying Ji

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

In the V-cycle of distributed electric wheel loaders (DEWLs), transport accounts for about 70% of the cycle, making energy saving urgent, while shovel-stage slip limits traction stability. This paper proposes a two-module control framework: (i) a preferred-motor transport strategy that reduces parasitic losses and concentrates operation in high-efficiency regions; and (ii) a load-ratio-based front–rear torque distribution for shoveling that allocates tractive effort according to instantaneous axle vertical loads so that each axle’s torque respects its available adhesion. For observability, we deploy a pre-calibrated lookup-table (LUT) mapping from bucket cylinder pressure to the front-axle load ratio, derived offline from a back-propagation neural network (BP-NN) fit. Tests on a newly developed DEWL show that, compared with dual-motor fixed-ratio control, transport-stage mechanical and electrical power drop by 18–37%, and drive-system efficiency rises by 6–13%. During shoveling, the strategy reduces the peak inter-axle slip from 22–35% to 13–15% and lowers the mean slip to 2.6–5.9%, suppressing sawtooth-like wheel-speed oscillations without sacrificing peak capacity. The method reduces parasitic energy flow, improves traction utilization, and is readily deployable.

Suggested Citation

  • Wenlong Shen & Shenrui Han & Xiaotao Fei & Yuan Gao & Changying Ji, 2025. "Improving Energy Efficiency and Traction Stability in Distributed Electric Wheel Loaders with Preferred-Motor and Load-Ratio Strategies," Energies, MDPI, vol. 18(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4969-:d:1752763
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    References listed on IDEAS

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    1. Yang, Yandong & Hong, Weijun & Li, Shufang, 2019. "Deep ensemble learning based probabilistic load forecasting in smart grids," Energy, Elsevier, vol. 189(C).
    2. Zhang, Haoxiang & Wang, Feng & Wu, Jiaming & Xu, Bing & Geimer, Marcus, 2025. "A cycle-adaptive control strategy to minimize electricity and battery aging costs of electric-hydraulic hybrid wheel loaders," Energy, Elsevier, vol. 319(C).
    3. Cemil Ocak, 2023. "A FEM-Based Comparative Study of the Effect of Rotor Bar Designs on the Performance of Squirrel Cage Induction Motors," Energies, MDPI, vol. 16(16), pages 1-17, August.
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