Abstract

This article proposes a hierarchical energy management strategy for power-split hybrid electric vehicles (HEVs) in presence of driving cycle uncertainty. The proposed hierarchical controller exploits long-term and short-term decision making via a high-level pseudospectral optimal controller and a low-level robust tube-based model predictive controller. This way, the proposed controller aims at robust charge balance constraint satisfaction and improvement in energy efficiency of the HEVs in presence of uncertainty in the future driving cycle. This article further focuses on the human-driven HEV energy management and exploits a data-driven future velocity prediction method that uses the data obtained from a drive simulator. Simulation results show an improvement in fuel economy for the proposed controller that is real time applicable and robust to the driving cycle’s uncertainty.

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