  This paper presents an online-capable controller for the energy management
system of a parallel hybrid electric vehicle based on model predictive control.
Its task is to minimize the vehicle's fuel consumption along a predicted
driving mission by calculating the distribution of the driver's power request
between the electrical and the combustive part of the powertrain, and by
choosing the driving mode, which depends on the vehicle's clutch state. The
inclusion of the clutch state in a model predictive control structure is not
trivial because the underlying optimization problem becomes a mixed-integer
program as a consequence. Using Pontryagin's Minimum Principle and a simplified
vehicle model, it is possible to prove that a drive cycle-dependent critical
power request Pcrit exists, which uniquely separates the optimal driving mode.
Based on this result, a learning algorithm is proposed to determine Pcrit
during the operation of the vehicle. The learning algorithm is incorporated
into a multi-level controller structure and the working principle of the
resulting multi-level learning-based model predictive controller is analyzed in
detail using two realistic driving missions. A comparison to the solution
obtained by Dynamic Programming reveals that the proposed controller achieves
close-to-optimal performance.
