Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

Philip K. Agyeman, Gangfeng Tan (), Frimpong J. Alex, Jamshid F. Valiev, Prince Owusu-Ansah, Isaac O. Olayode and Mohammed A. Hassan
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Philip K. Agyeman: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Gangfeng Tan: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Frimpong J. Alex: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jamshid F. Valiev: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Prince Owusu-Ansah: Mechanical Engineering Department, Faculty of Engineering, Kumasi Technical University, Kumasi 00233, Ghana
Isaac O. Olayode: Mechanical and Industrial Engineering Technology Department, University of Johannesburg, Johannesburg P.O. Box 2028, South Africa
Mohammed A. Hassan: Automotive and Tractors Engineering Department, Faculty of Engineering, Minia University, EI-Minia 61519, Egypt

Energies, 2022, vol. 15, issue 19, 1-23

Abstract: Based on the requisition for an ideal precise power source for a hybrid electric emergency rescue vehicle (HE-ERV), we present an optimistic parameter matching and optimization schemes for the selection of a HE-ERV. Then, given a set of optimized power source components, they are classified into different types of HE-ERV. In this study, due to the different design objectives of different types of emergency rescue vehicles and the problems of hybrid electric vehicle parameter matching, a multi-island genetic algorithm (MIGA) and non-linear programming quadratic Lagrangian (NLPQL) is proposed for the matched parameters. The vehicle dynamic model is established based on the AVL Cruise simulation platform. The power source performance parameters are matched by theoretical analysis and coupled to the simulation platform. Finally, the optimized matched parameters are classified based on the support vector machines classification model to determine the category of the HE-ERV. The classification results showed that there is an unprecedented level for categorizing several factors of the power source parameters. This research showed that its more logical and reasonable to match HE-ERVs with medium motor/engine power output and battery capacity, as these can attain dynamic performance, extended driving range, and reduced energy consumption.

Keywords: parameter matching; emergency rescue vehicle; multi-island genetic algorithm; non-linear programming quadratic lagrangian; support vector machines; gaussian mixture model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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