Model predictive control for integrated longitudinal and lateral stability of electric vehicles with in-wheel motors

This study investigates an integrated wheel slip, yaw rate, and sideslip angle control via torque vectoring to improve both the longitudinal and lateral stability of electric vehicles (EVs) with four in-wheel motors. The algorithm is developed based on model predictive control (MPC) and thus can optimally reach a balance among different objectives while considering actuation and state constraints. Firstly, to deal with tyre non-linearity and variations in the lateral tyre forces due to changes in tyre slip ratios, the mechanism of using torque vectoring to improve vehicle stability is analysed. Then, a non-linear tyre model is introduced into the predictive model to characterise the tyre force coupling relationship. Here, a linear-parameter-varying (LPV) model is employed, which is derived by linearising the nonlinear vehicle model online. Moreover, the stability control of EVs with in-wheel motors is transformed into a constrained online optimisation problem and solved using the proposed LPV-MPC method. Finally, the proposed LPV-MPC is compared with some existing well-established techniques from literature in different test scenarios. The obtained results demonstrate that the LPV-MPC approach could reduce the computational burden and shows a precise longitudinal control and obviously improves the lateral stability.