Abstract

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.

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Recent/v. various advanced methmis /or upscaling of standard material io High Drfiniiiun Televicion (HDTV) have been proposed. In 1hi.s paper. tlie translation of t l w e rrI~-.scaling techniyues fur application in intra:/ield deinterlacing has been investigated. In the evaliratiun, the proposed met had^ and the more common edge adaptive deinterlacing techniyires are compared.

No abstract