Energy management in hybrid electric vehicles : benefit of prediction

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings 8th International Symposium & Transmission Expo Innovative Fahrzeug-Getriebe, 30 November - 3 December 2009, Berlin, Germany
Pages1-11
Publication statusPublished - 2009

Fingerprint

Dive into the research topics of 'Energy management in hybrid electric vehicles : benefit of prediction'. Together they form a unique fingerprint.

Cite this