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

Wenlong Shen, Shenrui Han, Xiaotao Fei, Yuan Gao and Changying Ji ()
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Wenlong Shen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, 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

Energies, 2025, vol. 18, issue 18, 1-26

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.

Keywords: distributed electric wheel loader; control strategy; inter-axle slip; back-propagation neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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