Learning-Based Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Driving Mode Decisions

Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

“…It is used to ensure that the optimization problem remains feasible, even if an unrealistically high power request is predicted. A detailed explanation is provided in [20]. Third, there is the scalar term κ NO x • ϵ NO x , which, together with Equation (27g,j), represents a soft constraint on accumulated NO eo x .…”

“…In this section, the proposed control architecture to calculate the vehicle control inputs is presented. The overall controller architecture is presented in Figure 11 and is based on the work presented in [20]. The controller consists of three controller levels: the lower-level controller; the so-called MPC; and the reference trajectory generator, which is from here on referred to as the RTG.…”

“…The MPC also consists of a map, denoted in Figure 11 by the block Map, which is defined on the ( Preq − v) plane, and the COP. This map is not static but is adapted over time by using a learning algorithm, which is explained in detail in [20]. The controller is updated every T MPC s seconds, where each update includes the following steps: The map is used to estimate the time-resolved optimal driving mode M based on the predicted power request.…”

“…This is performed by using interpolation of the spatially defined vectors SoC RTG (s) and m RTG NO x (s) and the predicted vehicle position s(t 0 + H p ) at the end of the prediction horizon. The two costates λ SoC and λ NO x are obtained by solving the COP [20].…”