Mode transition prescribed performance control of connected hybrid electric vehicles based on driver intention fuzzy prediction

Abstract

A novel hierarchical mode transition control (NHMTC) algorithm utilizing traffic information is investigated for realizing a superior dynamic and ride comfort performance of a connected hybrid electric vehicle (CHEV). The upper level includes the target vehicle velocity planning by model predictive control (MPC) considering less vehicle jerk based on the vehicle-to-infrastructure (V2I) information and the driving intention prediction by a type-2 fuzzy neural network (T2FNN) model using the planned target velocity. The lower level involves the automatic clutch system’s prescribed performance fixed-time controller (PPFTC) design and the desired velocity of the clutch engagement calculated by the predicted driving intention to improve the clutch position trajectory’s tracking accuracy and stability under the actual mode transition process. The comparison results with existing schemes under a co-simulation environment demonstrate the advantage and effectiveness of the designed NHMTC method.