A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was...A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.展开更多
Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybri...Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-展开更多
文摘A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.
基金supported by the Natural Science Foundation of Hubei Province(Grant No.2015CFB586)
文摘Improvements in fuel consumption and emissions of hybrid electric vehicle(HEV)heavily depend upon an efficient energy management strategy(EMS).This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle em-
