Meta-model-based optimization of rule-based energy management in second-hand plug-in hybrid electric vehicles

Abstract

This study presents a methodology to enhance energy management systems (EMS) in hybrid electric vehicles (HEVs) to reduce fuel consumption and greenhouse gas emissions. A novel surrogate-assisted optimization framework is employed, incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS. These models are optimized using various algorithms targeting parameters such as engine idle speed, thermostat temperature fraction, regeneration load factor, and battery state-of-charge thresholds. Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions. Among the optimization methods, the combination of a backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) proves most effective, achieving fuel consumption reductions of 5.26% and 5.01% in charge-sustaining and charge-depletion modes, respectively. Additionally, the BPNN-based MOGA demonstrates notable improvements in emission reduction. These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.