The multimode power-split architecture for hybrid electric vehicle (HEV) powertrains is generally known for the complexity of its operation. This paper firstly addresses the challenge of developing an automated on-line near-optimal control strategy for these systems. Particularly, a machine learning logic based on supervised learning is developed for on-line selection of the HEV operating mode, thus the particular set of clutches to be engaged. An efficiency-based approach is then adopted to determine the optimal power-split between powertrain components. Later, the developed strategy finds integration in an optimal design methodology for multimode power-split HEVs considering the effectiveness and ease of on-line controllability. The obtained results are compared with the ones by the traditional HEV design methodology that considers off-line energy management only. The illustrated design methodology with on-line control reveals efficient at identifying the multimode HEV design that demonstrates optimal predicted fuel economy values and ease of on-line controllability simultaneously. Results suggest that the HEV design optimization procedure may produce different outcomes and demonstrate more effective when the evaluation of the on-line controlled operation is integrated.
Integration of On-line Control in Optimal Design of Multimode Power-split Hybrid Electric Vehicle Powertrains / Anselma, Pier Giuseppe; Huo, Yi; Roeleveld, Joel; Belingardi, Giovanni; Emadi, Ali. - In: IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. - ISSN 0018-9545. - 68:4(2019), pp. 3436-3445.
|Titolo:||Integration of On-line Control in Optimal Design of Multimode Power-split Hybrid Electric Vehicle Powertrains|
|Data di pubblicazione:||2019|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/TVT.2019.2901901|
|Appare nelle tipologie:||1.1 Articolo in rivista|