Abstract
In this paper a real-time capable hierarchical nonlinear model predictive control framework for the energy management of a hybrid electric vehicle is presented. As high-level energy management, nonlinear model predictive control is employed. Therefore, a nonlinear optimal control problem is formulated using a control-oriented internal model derived from high-fidelity models and experimental data. The multiple shooting algorithm and an Euler backward scheme are used to discretize the optimal control problem. The resulting nonlinear problem is solved in real-time using Sequential Quadratic Programming. A rule-based gear choice and engine on / off strategy is added. Actuator dynamics and drivability are addressed in a fast low-level linear time-variant model predictive controller. The result is analyzed and compared to the optimal solution obtained by dynamic programming using a simplified model of the vehicle.
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