English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents an online-capable controller for the energy managementsystem of a parallel hybrid electric vehicle based on model predictive control.Its task is to minimize the vehicle's fuel consumption along a predicteddriving mission by calculating the distribution of the driver's power requestbetween the electrical and the combustive part of the powertrain, and bychoosing the driving mode, which depends on the vehicle's clutch state. Theinclusion of the clutch state in a model predictive control structure is nottrivial because the underlying optimization problem becomes a mixed-integerprogram as a consequence. Using Pontryagin's Minimum Principle and a simplifiedvehicle model, it is possible to prove that a drive cycle-dependent criticalpower request Pcrit exists, which uniquely separates the optimal driving mode.Based on this result, a learning algorithm is proposed to determine Pcritduring the operation of the vehicle. The learning algorithm is incorporatedinto a multi-level controller structure and the working principle of theresulting multi-level learning-based model predictive controller is analyzed indetail using two realistic driving missions. A comparison to the solutionobtained by Dynamic Programming reveals that the proposed controller achievesclose-to-optimal performance.

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