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Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle

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Listed:
  • da Silva, Samuel Filgueira
  • Eckert, Jony Javorski
  • Corrêa, Fernanda Cristina
  • Silva, Fabrício Leonardo
  • Silva, Ludmila C.A.
  • Dedini, Franco Giuseppe

Abstract

This study presents a comprehensive multi-objective optimization approach for a dual HESS-based electric vehicle (EV) powertrain using the interactive adaptive-weight genetic algorithm (i-AWGA) method. The dual HESS EV concept aims to show the benefits of combining independent traction systems powered by their respective energy sources. Therefore, the main purpose of this optimization is to simultaneously maximize driving autonomy and battery lifespan and minimize HESS size, considering design variables from components such as batteries, electric motors, differential, and ultracapacitors. At the same time, three independent fuzzy logic controllers – which perform the power management control between the hybrid energy storage systems – are likewise optimized, tuning their parameters according to the applied constraints. The best trade-off solution, equipped with a 332.34 kg dual HESS mass, achieved a driving range of 285.56 km and a front battery life cycle of 36585 h. As compared to a similar EV powered by a single HESS and optimized under the same driving conditions, the dual HESS EV improved the ratio between the driving range and energy storage system’s overall mass by 3%, reaching a driving range 19.57% longer, and increasing the battery life by up to 22.88%.

Suggested Citation

  • da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010121
    DOI: 10.1016/j.apenergy.2022.119723
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    Cited by:

    1. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    2. Huang, Jiangfan & An, Qing & Zhou, Mingyu & Tang, Ruoli & Dong, Zhengcheng & Lai, Jingang & Li, Xin & Yang, Xiangguo, 2024. "A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics," Applied Energy, Elsevier, vol. 361(C).
    3. Chen, Bin & Wang, Miaoben & Hu, Lin & He, Guo & Yan, Haoyang & Wen, Xinji & Du, Ronghua, 2024. "Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios," Applied Energy, Elsevier, vol. 365(C).
    4. Joseph Omakor & Mohamad Alzayed & Hicham Chaoui, 2024. "Particle Swarm-Optimized Fuzzy Logic Energy Management of Hybrid Energy Storage in Electric Vehicles," Energies, MDPI, vol. 17(9), pages 1-19, April.
    5. Cui, Wei & Cui, Naxin & Li, Tao & Du, Yi & Zhang, Chenghui, 2024. "Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction," Applied Energy, Elsevier, vol. 360(C).
    6. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).

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