To enhance the handling stability of distributed drive electric vehicles (DDEVs) under high-speed conditions and different road surfaces, this paper proposes a hierarchical integrated control strategy utilizing hybrid model predictive control (HMPC). The approach begins with constructing a tire model through a piecewise affine (PWA) method, followed by developing a prediction model using a mixed logic dynamic (MLD) modeling technique combined with logical propositional methods. The proposed integrated control strategy includes an upper controller that utilizes the HMPC strategy. A mixed integer quadratic programming method is employed to track the target value and determine the additional yaw moment and active steering angle. The lower controller is designed to optimize the drive torque to minimize the tire load rate. Carsim/Simulink simulation platform and vehicle test platform were built, respectively, to verify the superiority of the integrated control strategy through a double-lane change test on different road surfaces. The vehicle test results demonstrate that under high-speed and low adhesion conditions, the proposed integrated control strategy effectively reduced error of the yaw rate and sideslip angle by 6.52% and 10.76%, respectively, compared to linear model predictive control (MPC). Additionally, under high adhesion conditions, the lateral displacement error of HMPC is reduced by 8.16%. In low adhesion conditions, where uncontrolled conditions can lead to severe vehicle instability, HMPC reduces the peak error by 10.22% compared to linear MPC.
Hybrid model predictive control-based integration of handling stability control for distributed drive electric vehicles
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Europäisches Patentamt | 2025
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