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Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network

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  • Lin, Xinyou
  • Zeng, Songrong
  • Li, Xuefan

Abstract

The performance of predictive energy management (PEM) strategy depends greatly on the precise prediction. Inevitably, the imprecise prediction occurs under changeable driving conditions. This study proposes an online correction predictive energy management strategy to address the above issue. The first is development of a PEM using the sequential quadratic programming (SQP) combined with back-propagation neural network-based velocity prediction. Second, a correction algorithm based Fuzzy Neural Network (FNN) is designed to evaluate the correction factor. The Q-learning based Swarm Optimization (QSO) algorithm is implemented to optimize the parameters of the FNN and evaluate accurately the correction factor. Finally, in combination with the above efforts, a novel correction algorithm based on the SQP backup control strategy and integrated the QSO algorithm with the FNN has been further established accordingly. The numerical validation results demonstrate that the superior performance of the proposed strategy in economic compared with the benchmark strategies, and the effectiveness of the proposed strategy is validated by Hardware-in-the-loop (HIL) experiments. Both the numerical validation and HIL results indicate that the combination of QSO and FNN made it possible to develop the online correction algorithm capable of significantly further improving the hydrogen consumption of the fuel cell electric vehicle.

Suggested Citation

  • Lin, Xinyou & Zeng, Songrong & Li, Xuefan, 2021. "Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221003200
    DOI: 10.1016/j.energy.2021.120071
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    3. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    4. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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