Rule-based Control and Equivalent Consumption Minimization Strategies for Hybrid Electric Vehicle Powertrains: a Hardware-in-the-loop Assessment

Abstract

Energy management systems are crucial in hybrid electric vehicles (HEVs). Other than enhanced energy economy, a proper energy management system must guarantee acceptable driving comfort, compliance with the allowed battery state-of-charge window, and on-board computational efficiency. While several studies from the literature have compared different state-of-the-art real-time HEV powertrain energy management strategies, not much work has been performed on the hardware-in-the-loop (HIL) assessment of these control approaches. This paper aims at answering the identified research need by performing an experimental HIL assessment of different state-of-the-art HEV control strategies including a rule-based control (RBC) approach and three different formulations of equivalent consumption minimization strategy (ECMS), both of traditional and adaptive type. A parallel-through-the-road HEV is considered for this case study. Various assessment criteria are retained including HEV fuel economy, measured computational time, and comfort of the ride in terms of frequency of de/activation events and smoothness of the controlled value of torque over time for the internal combustion engine. Obtained results suggest that the RBC approach can achieve improved performance in almost all the retained evaluation criteria. The traditional ECMS can outperform RBC in terms of fuel economy, yet by undermining both ride comfort and compliance with the battery SOC window. Finally, an adaptive ECMS can outperform the RBC in terms of fuel economy while ensuring acceptable comfort and compliance with the battery SOC window, yet at a significant computational cost increase.