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Deep learning application in fuel cell electric bicycle to optimize bicycle performance and energy consumption under the effect of key input parameters

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  • Hieu, Le Trong
  • Lim, Ock Taeck

Abstract

The objective of this paper is to optimize the energy consumption and performance of fuel cell electric bicycles (FCEBs) under specific key input parameters. The paper applied an integrated method that includes an artificial neural network (ANN) and genetic algorithm (GA) to forecast and identify an optimal performance and energy consumption of FCEBs. The simulation model of FCEBs is established and simulated in MATLAB-Simulink environment to generate 1000 data points, that are used for training, validating, testing artificial neural network, the ANN architecture containing five input neurons, two hidden neurons and two output neurons, respectively. Furthermore, the GA is integrated to find the maximum performance and energy consumption once the ANN has exactly been trained. The study found that the FCEB configuration can achieve an effective performance at 30.3 km/h with required power of 210.4 W under speed level_5, radius of wheel 0.39 m, frontal area 0.423 m2, slope grade 0%. In order to validate and verify the simulated results, the experimental approach method was conducted in the same condition. The experimental results fit well with the simulated results in the same initial input parameters.

Suggested Citation

  • Hieu, Le Trong & Lim, Ock Taeck, 2024. "Deep learning application in fuel cell electric bicycle to optimize bicycle performance and energy consumption under the effect of key input parameters," Applied Energy, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924009711
    DOI: 10.1016/j.apenergy.2024.123588
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    References listed on IDEAS

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    1. Minutillo, M. & Forcina, A. & Jannelli, N. & Lubrano Lavadera, A., 2018. "Assessment of a sustainable energy chain designed for promoting the hydrogen mobility by means of fuel-cell powered bicycles," Energy, Elsevier, vol. 153(C), pages 200-210.
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    3. Kheirandish, Azadeh & Motlagh, Farid & Shafiabady, Niusha & Dahari, Mahidzal & Khairi Abdul Wahab, Ahmad, 2017. "Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system," Applied Energy, Elsevier, vol. 202(C), pages 20-31.
    4. Hu, Jingfan & Zheng, Wandong & Zhang, Sirui & Li, Hao & Liu, Zijian & Zhang, Guo & Yang, Xu, 2021. "Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control," Applied Energy, Elsevier, vol. 300(C).
    5. Le Trong Hieu & Ock Taeck Lim, 2023. "Effects of the Structure and Operating Parameters on the Performance of an Electric Scooter," Sustainability, MDPI, vol. 15(11), pages 1-19, June.
    Full references (including those not matched with items on IDEAS)

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