TY - JOUR
T1 - Genetic-fuzzy HEV control strategy based on driving cycle recognition
AU - Xing, Jie
AU - He, Hongwen
AU - Zhang, Xiaowei
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
KW - Driving cycle recognition (DCR)
KW - Fuzzy logic control (FLC)
KW - Genetic algorithm (GA) optimization
KW - HEV control strategy
KW - Neural algorithm optimization
UR - https://www.scopus.com/pages/publications/77952226299
U2 - 10.3772/j.issn.1006-6748.2010.01.008
DO - 10.3772/j.issn.1006-6748.2010.01.008
M3 - Article
AN - SCOPUS:77952226299
SN - 1006-6748
VL - 16
SP - 39
EP - 44
JO - High Technology Letters
JF - High Technology Letters
IS - 1
ER -