Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

Date published

2015-07-20

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Publisher

Taylor and Francis

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Article

ISSN

0042-3114

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Citation

Efstathios Siampis, Efstathios Velenis and Stefano Longo. Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility. Volume 53, Issue 11, 2015. pp1555-1579

Abstract

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment.

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Github

Keywords

torque vectoring, model predictive control, combined velocity, yaw and sideslip control, limit handling

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