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 ...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 stateof-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.展开更多
文摘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 stateof-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.
