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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 33, Number 1(3) (2021)
Copyright(C) MYU K.K.
pp. 301-313
S&M2455 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3013
Published: January 31, 2021

Research on Novel Fuzzy Control Strategy of Hybrid Electric Vehicles Based on Feature Selection Genetic Algorithm [PDF]

Tianjun Zhu, Linglong Wang, Xiaoxiang Na, Tunglung Wu, Wei Hu, and Rouchun Jiang

(Received May 17, 2020; Accepted November 16, 2020)

Keywords: fuzzy control strategy, feature selection genetic algorithm, fuel economy, emission

We propose a novel fuzzy control strategy for hybrid electric vehicles (HEVs) based on the feature selection genetic algorithm of multivariate data, which greatly shortens the selection time of the optimal parameters of the traditional genetic algorithm. Firstly, we take the fuel consumption and emission of an HEV as the optimization index, and develop a novel fuzzy control method considering parameters of the fuzzy controller with high correlation with the objective function, in which the membership function parameter is optimized by the feature selection genetic algorithm. Finally, the performances of the fuzzy control strategy for an HEV and the novel fuzzy control strategy optimized by the feature selection genetic algorithm under the New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS) cycle conditions are analyzed and compared. The results show that the proposed fuzzy control can greatly improve the fuel economy and reduce the emission of HEVs.

Corresponding author: Tianjun Zhu


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Cite this article
Tianjun Zhu, Linglong Wang, Xiaoxiang Na, Tunglung Wu, Wei Hu, and Rouchun Jiang, Research on Novel Fuzzy Control Strategy of Hybrid Electric Vehicles Based on Feature Selection Genetic Algorithm, Sens. Mater., Vol. 33, No. 1, 2021, p. 301-313.



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