Multi-Objective Energy-Efficient Driving for Four-Wheel Hub Motor Unmanned Ground Vehicles

Yongjuan Zhao, Jiangyong Mi, Chaozhe Guo, Haidi Wang, Lijin Wang and Hailong Zhang ()
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Yongjuan Zhao: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China
Jiangyong Mi: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China
Chaozhe Guo: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China
Haidi Wang: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China
Lijin Wang: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China
Hailong Zhang: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China

Energies, 2025, vol. 18, issue 17, 1-27

Abstract: Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles.

Keywords: unmanned ground vehicles; model predictive control; hierarchical control architecture; energy-efficient driving; multi-objective optimization (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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