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Powertrain Matching and Optimization of Dual-Motor Hybrid Driving System for Electric Vehicle Based on Quantum Genetic Intelligent Algorithm

Author

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  • Yong Wang
  • Dongye Sun

Abstract

In order to increase the driving range and improve the overall performance of all-electric vehicles, a new dual-motor hybrid driving system with two power sources was proposed. This system achieved torque-speed coupling between the two power sources and greatly improved the high performance working range of the motors; at the same time, continuously variable transmission (CVT) was achieved to efficiently increase the driving range. The power system parameters were determined using the “global optimization method”; thus, the vehicle’s dynamics and economy were used as the optimization indexes. Based on preliminary matches, quantum genetic algorithm was introduced to optimize the matching in the dual-motor hybrid power system. Backward simulation was performed on the combined simulation platform of Matlab/Simulink and AVL-Cruise to optimize, simulate, and verify the system parameters of the transmission system. Results showed that quantum genetic algorithms exhibited good global optimization capability and convergence in dealing with multiobjective and multiparameter optimization. The dual-motor hybrid-driving system for electric cars satisfied the dynamic performance and economy requirements of design, efficiently increasing the driving range of the car, having high performance, and reducing energy consumption of 15.6% compared with the conventional electric vehicle with single-speed reducers.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:jnddns:956521
    DOI: 10.1155/2014/956521
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    Cited by:

    1. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. Tian, Yang & Zhang, Yahui & Li, Hongmin & Gao, Jinwu & Swen, Austin & Wen, Guilin, 2023. "Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles," Energy, Elsevier, vol. 275(C).
    3. Jixiang Yang & Yongming Bian & Meng Yang & Jie Shao & Ao Liang, 2021. "Parameter Matching of Energy Regeneration System for Parallel Hydraulic Hybrid Loader," Energies, MDPI, vol. 14(16), pages 1-26, August.
    4. Li, Yapeng & Tang, Xiaolin & Lin, Xianke & Grzesiak, Lech & Hu, Xiaosong, 2022. "The role and application of convex modeling and optimization in electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    5. Wang, Zhenzhen & Zhou, Jun & Rizzoni, Giorgio, 2022. "A review of architectures and control strategies of dual-motor coupling powertrain systems for battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. 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.
    7. Shaohua Cui & Hui Zhao & Cuiping Zhang, 2018. "Locating Charging Stations of Various Sizes with Different Numbers of Chargers for Battery Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-22, November.

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