RT Journal Article
A1 Qiwei Xu
A1 Xiaoxiao Luo
A1 Xiaobiao Jiang
A1 Meng Zhao

PB iet
T1 Research on double fuzzy control strategy for parallel hybrid electric vehicle based on GA and DP optimisation
JN IET Electrical Systems in Transportation
VO 8
IS 2
SP 144
OP 151
AB In this study, a double fuzzy control strategy for the parallel hybrid electric vehicle (HEV) is proposed, then based on the genetic algorithm (GA) to get better simulation results, and the results are verified by dynamic programming (DP) optimisation. First, the energy management strategy is established by fuzzy control theory. On this basis, considering braking energy recovery, this study designs a double fuzzy vehicle energy control strategy. A simulation analysis of the above two control strategies is carried out in urban dynamometer driving schedule, and the comparison with the work efficiency of the engine and fuel economy performance, respectively, is made; the simulation results show that the double fuzzy control strategy can effectively improve the HEV performance. In order to make rule base more accurate, this study also uses a GA to optimise the fuzzy control rules of the fuzzy controller. Then the DP is used to optimise the energy control strategy and obtain optimal results. The results verified that the design of fuzzy controllers is correct, and the optimised fuzzy control strategy by GA can improve the work efficiency of the engine and fuel consumption.
K1 genetic algorithm
K1 GA optimisation
K1 HEV performance
K1 DP optimisation
K1 fuel economy performance
K1 parallel hybrid electric vehicle
K1 simulation analysis
K1 work engine efficiency
K1 urban dynamometer driving schedule
K1 double fuzzy vehicle energy control strategy
K1 braking energy recovery
K1 dynamic programming
K1 energy management strategy
DO https://doi.org/10.1049/iet-est.2017.0067
UL https://digital-library.theiet.org/;jsessionid=3ds2dndse7j2v.x-iet-live-01content/journals/10.1049/iet-est.2017.0067
LA English
SN 2042-9738
YR 2018
OL EN