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

Author

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  • Philip K. Agyeman

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Mechanical Engineering Department, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi 03220, Ghana)

  • 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
    Mechanical Engineering Department, Faculty of Engineering, Kumasi Technical University, Kumasi 00233, Ghana)

  • 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)

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.

Suggested Citation

  • Philip K. Agyeman & Gangfeng Tan & Frimpong J. Alex & Jamshid F. Valiev & Prince Owusu-Ansah & Isaac O. Olayode & Mohammed A. Hassan, 2022. "Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines," Energies, MDPI, vol. 15(19), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7071-:d:925684
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    References listed on IDEAS

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    1. Yong Wang & Dongye Sun, 2014. "Powertrain Matching and Optimization of Dual-Motor Hybrid Driving System for Electric Vehicle Based on Quantum Genetic Intelligent Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-11, November.
    2. Bhattacharjee, Debraj & Ghosh, Tamal & Bhola, Prabha & Martinsen, Kristian & Dan, Pranab K., 2019. "Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance," Energy, Elsevier, vol. 183(C), pages 235-248.
    3. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    4. Pengxiang Song & Yulong Lei & Yao Fu, 2020. "Multi-Objective Optimization and Matching of Power Source for PHEV Based on Genetic Algorithm," Energies, MDPI, vol. 13(5), pages 1-20, March.
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    Cited by:

    1. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.

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