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Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle

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  • Miranda, Matheus H.R.
  • Silva, Fabrício L.
  • Lourenço, Maria A.M.
  • Eckert, Jony J.
  • Silva, Ludmila C.A.

Abstract

The four-wheel independent-drive electric vehicle (FWID-EV) stands out among the electrically-propelled vehicles due to its higher efficiency. This EV configuration faces an energy management challenge, needing a controller to define the best power split among the motors, decreasing the energy consumption while keeping the EV performance. Aiming to overcome this challenge, this paper presents a fuzzy logic control (FLC) strategy to perform the power split among the electric motors to improve vehicle energy efficiency and dynamic performance under real driving conditions. The implemented controller is developed hierarchically, ensuring the speed follows a driving cycle and acting as an electronic differential to correct the vehicle trajectory along the path. Multi-objective optimization is presented using a particle swarm algorithm (MOPSO) to minimize the mass of the electrical components (motors and battery) and extend the driving range throu gh the maximization of the battery state of charge (SoC). Moreover, the optimization process also aims to enhance vehicle handling by minimizing the driver steering input during the cycle. The best trade-off solution was able to reach a 124.2 km drive range with a 235.8 kg battery (387.8 V and 91.2 Ah), presenting improved handling performance with 78.5% decrease in the driver steering action.

Suggested Citation

  • Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022271
    DOI: 10.1016/j.energy.2021.121979
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    References listed on IDEAS

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    3. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
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    5. 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).
    6. Aminu Babangida & Chiedozie Maduakolam Light Odazie & Péter Tamás Szemes, 2023. "Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle," Mathematics, MDPI, vol. 11(15), pages 1-19, August.
    7. Diego Castanho & Marcio Guerreiro & Ludmila Silva & Jony Eckert & Thiago Antonini Alves & Yara de Souza Tadano & Sergio Luiz Stevan & Hugo Valadares Siqueira & Fernanda Cristina Corrêa, 2022. "Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization," Energies, MDPI, vol. 15(19), pages 1-21, September.
    8. Hongtu Yang & Yan Sun & Changgao Xia & Hongdang Zhang, 2022. "Research on Energy Management Strategy of Fuel Cell Electric Tractor Based on Multi-Algorithm Fusion and Optimization," Energies, MDPI, vol. 15(17), pages 1-15, September.
    9. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    10. Huang, Xiaohui & Huang, Qi & Cao, Huajun & Yan, Wanbin & Cao, Le & Zhang, Qiongzhi, 2023. "Optimal design for improving operation performance of electric construction machinery collaborative system: Method and application," Energy, Elsevier, vol. 263(PA).

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