Abstract
Hybrid vehicles require a supervisory algorithm, often referred to as Energy
Management Strategy (EMS), which governs the drivetrain components. In general the
EMS objective is to minimize the fuel consumption subject to constraints on the
components, vehicle performance and driver comfort. Typically, we have to deal with two
difficulties in the design of EMS. First, the nonlinear behavior of the components results
in a nonconvex cost function, complicating the use of optimization methods. In this
paper, different approaches to deal with the nonconvexity are discussed. Secondly, the
future power request trajectory is unknown. Prediction of the future power request
trajectory, based upon either past or predicted vehicle velocity and road grade
trajectories, could help in obtaining a solution close to optimal. The benefit of prediction,
compared to a heuristic and optimal control strategy that uses only actual vehicle data, is
shown with an example of a hybrid truck in a highway trajectory in a hilly environment.
Results indicate that prediction has benefit only when the slopes have sufficient grade
and length, such that the battery state-of-charge boundaries are reached.
Original language | English |
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Title of host publication | Proceedings 8th International Symposium & Transmission Expo Innovative Fahrzeug-Getriebe, 30 November - 3 December 2009, Berlin, Germany |
Pages | 1-11 |
Publication status | Published - 2009 |