摘要：AbstractIn this paper a nonlinear model predictive controller for the optimization of thehybrid electric vehicle fuel efficiency is introduced. The idea is to use the concept of differential atness to determine the evolution of system's state across the prediction horizon without numerical integration. The entire nonlinear optimization problem is expressed as a function of a fictious output, called the at output and its first derivative. The optimal distribution of the requested torque between propulsion devices is then determined by a static optimization. The atness-based model predictive controller (FMPC) is compared to a linear time-varying MPC (LTV-MPC) which applies the linearized model of the plant to predict the future state and output trajectories. Both strategies are evaluated on standard driving cycles. To proof the concept it is assumed that the velocity profile and driver torque demand are known and exact over a predefined prediction horizon
关键词：Keywordshybrid electric vehiclesenergy managementnonlinear MPCflatnessB-splines
