Abstract—With the increasing environmental requirements in the mining industry, battery-powered underground Load- Haul-Dump (LHD) machines have become mainstream mining equipment due to their zero emissions and low noise. However, the limited battery capacity poses a critical challenge in improving energy utilization efficiency and extending the working time per charge. This study proposes an energy-saving strategy based on dual motor drive and intelligent gear-shifting control to optimize energy efficiency. Firstly, a model of the LHD’s drive system and energy consumption is established to analyze energy usage characteristics under different working conditions. Then, an adaptive genetic algorithm is employed to optimize transmission system parameters. Furthermore, an intelligent energy-saving gear-shifting strategy based on a Backpropagation Neural Network (BPNN) is proposed to achieve precise gear control and reduce energy consumption. Simulation results indicate that the proposed strategy reduces energy consumption per working cycle by 12.7%, significantly enhancing the economic efficiency and operational performance of the LHD. This research provides a theoretical basis and engineering reference for energysaving control of underground LHDs.

Keywords—battery-powered Load-Haul-Dump (LHD), Transmission system, Energy-saving optimization, Backpropagation Neural Network (BPNN), Intelligent gearshifting control

Cite: Chengsi Li and Surachai Hemhirun, "Energy-Saving Research on Battery-Powered Load-Haul-Dump Machine Based on Dual Motor Drive and Intelligent Gear-Shifting Control," International Journal of Mechanical Engineering and Robotics Research, Vol. 14, No. 4, pp. 418-429, 2025. doi: 10.18178/ijmerr.14.4.418-429

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).