Predictive Energy Management for an Electric Vehicle with Fuel Cell Range Extender in Connected Traffic Systems

This paper presents the design of a predictive energy management (pEEM) for an automated electric vehicle with fuel cell range extender in connected traffics systems. An essential task of the pEEM is the optimization of the power supply by a controlled power distribution to battery and fuel cell for the entire journey to minimize energy losses considering restrictions by operating limits or available energy. To solve this optimization problem, a nonlinear model predictive control structure is designed, since it considers the future system behavior on the one hand and is excellently suited for the integration of constraints or restrictions on the other hand. This control structure consists of several model predictive controllers (MPC), which cover different time horizons with different sampling times in a time cascaded structure. This structure reduces the accuracy of time periods far in the future, which are subject to high uncertainty. Furthermore, the computational effort is considerably reduced, so that a realization under real-time conditions is possible. The paper concludes with an exemplary application of power supply that demonstrates the functionality of the pEEM both in simulation and under real-time conditions on a HiL test bench.