The multimode power-split architecture for hybridelectric vehicle (HEV) powertrains is generally known for thecomplexity of its operation. This paper firstly addresses thechallenge of developing an automated on-line near-optimalcontrol strategy for these systems. Particularly, a machinelearning logic based on supervised learning is developed for online selection of the HEV operating mode, thus the particular setof clutches to be engaged. An efficiency-based approach is thenadopted to determine the optimal power-split betweenpowertrain components.Later, the developed strategy finds integration in an optimaldesign methodology for multimode power-split HEVs consideringthe effectiveness and ease of on-line controllability. The obtainedresults are compared with the ones by the traditional HEV designmethodology that considers off-line energy management only.The illustrated design methodology with on-line control revealsefficient at identifying the multimode HEV design thatdemonstrates optimal predicted fuel economy values and ease ofon-line controllability simultaneously. Results suggest that theHEV design optimization procedure may produce differentoutcomes and demonstrate more effective when the evaluation ofthe on-line controlled operation is integrated.
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