The distributed drive electric platform is extensively used in special-purpose unmanned ground vehicles (UGVs) due to exceptional maneuverability and obstacle navigation capabilities in off-road environments. This article comes up with an energy-saving torque allocation strategy to enhance path-tracking precision and energy utilization efficiency. For mitigating the conflict between path tracking and energy management in torque distribution, a hierarchical control framework is proposed for total demanding torque control, inter-axle torque distribution, and inter-wheel torque allocation. The inter-axle torque distribution is determined by considering the total motors’ efficiency region reflection to enhance energy conversion efficiency. This approach ensures that torque is allocated to maximize energy conversion efficiency. Besides, the inter-wheel torque allocation is optimized using quadratic programming (QP), considering factors such as tire vertical load distribution and path tracking error. By decoupling these control aspects, the proposed framework ensures optimal performance in both path tracking and energy management. In contrast, a traditional torque allocation method is also presented. Finally, the simulation experiments are conducted in MATLAB/Simulink and TruckSim with specialized off-road working cycles. The numerical experimental results demonstrate that the presented strategy considerably ensures an optimal torque allocation for path tracking while simultaneously enhances the overall efficiency of the distributed drive system compared with the conventional approach.
Energy-Efficient Torque Allocation Strategy for Autonomous Distributed Drive Electric Vehicle
IEEE Transactions on Transportation Electrification ; 10 , 4 ; 8275-8285
2024-12-01
4740489 byte
Article (Journal)
Electronic Resource
English
AUTONOMOUS VEHICLE ENERGY EFFICIENT ELECTRIC TRACTION DRIVE
European Patent Office | 2018
|SAE Technical Papers | 2018
|British Library Conference Proceedings | 2019
|Torque allocation strategy for four in-wheel-motor drive electric vehicle based on layered control
SAGE Publications | 2022
|