Intelligent control of in-wheel motors in a vehicle with analytical determination of torque distribution

2026

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DOI 10.33271/nvngu/2026-4/068
Достоверность данных 70 средняя
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Аннотация

Purpose. To derive a closed-form analytical solution for the optimal distribution of drive torque between the left and right sides of an all-wheel-drive vehicle equipped with in-wheel motors. The proposed solution maximizes the instantaneous efficiency of the wheel propulsion system while accounting for load asymmetry and driving conditions. Methodology. The study employs analytical methods based on a reduced energy model of the wheel propulsion system, quadratic optimization techniques, and simulation modelling in the MATLAB/Simulink environment to verify the performance of the proposed torque distribution law. Findings. A closed-form analytical expression was obtained for determining the optimal torque distribution coefficient between the left and right sides of the vehicle. Its application ensures the maximization of the instantaneous efficiency of the wheel propulsion system. It was demonstrated that the optimal torque distribution depends on the normal tyre-road reactions, vehicle geometric parameters, and driving conditions. Simulation results confirmed the effectiveness of the proposed control law under both traction and curvilinear driving conditions. Originality. For the first time, the problem of torque distribution between the left and right sides of an all-wheel-drive vehicle with independently driven in-wheel motors has been formulated as the problem of maximizing the instantaneous efficiency of the wheel propulsion system. This made it possible to derive a control law suitable for real-time implementation without the need for numerical optimization. Practical value. The proposed analytical torque distribution law can be integrated into the upper-level control system of all-wheel-drive vehicles equipped with independently driven in-wheel motors. Its implementation improves vehicle energy efficiency without increasing the complexity of the control algorithms or the associated computational burden.