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Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

1
Department of Automotive Engineering, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
Guizhou Changjiang Automobile Co., Ltd., Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(2), 426; https://doi.org/10.3390/en13020426
Received: 23 November 2019 / Revised: 28 December 2019 / Accepted: 12 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.
Keywords: fuel cell hybrid electric vehicle; least squares support vector machines (LSSVM); driving conditions identification; power distribution fuel cell hybrid electric vehicle; least squares support vector machines (LSSVM); driving conditions identification; power distribution
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Zheng, Y.; He, F.; Shen, X.; Jiang, X. Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine. Energies 2020, 13, 426.

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