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Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles

Author

Listed:
  • Jiapeng Yan

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Huifang Kong

    (School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

  • Zhihong Man

    (School of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC 3122, Australia)

Abstract

In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP). Recurrent neural networks (RNNs) are advantageous in many folds for solving NOPs, yet existing RNNs’ convergence usually requires convexity with calculation of second-order partial derivatives. In this paper, a recurrent neural network-based NOP solver (RNN-NOPS) is developed. It is seen that the RNN-NOPS is designed to drive all state variables to asymptotically converge to the feasible region, with loose requirement on the NOP’s first-order partial derivative. In addition, the RNN-NOPS’s equilibria are proved to meet Karush–Kuhn–Tucker (KKT) conditions, and the RNN-NOPS behaves with a strong robustness against the violation of the constraints. The comparative studies are conducted to show RNN-NOPS’s advantages for solving the EHB force allocation problem, it is reported that the overall regenerative energy of RNN-NOPS is 15.39% more than that of the method for comparison under SC03 cycle.

Suggested Citation

  • Jiapeng Yan & Huifang Kong & Zhihong Man, 2022. "Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles," Energies, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9486-:d:1003373
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    References listed on IDEAS

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    3. I. V. Konnov, 1997. "A Class of Combined Iterative Methods for Solving Variational Inequalities," Journal of Optimization Theory and Applications, Springer, vol. 94(3), pages 677-693, September.
    4. Roman Tkachenko & Ivan Izonin & Pavlo Vitynskyi & Nataliia Lotoshynska & Olena Pavlyuk, 2018. "Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs," Data, MDPI, vol. 3(4), pages 1-14, October.
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    Cited by:

    1. Grzegorz Filo, 2023. "Artificial Intelligence Methods in Hydraulic System Design," Energies, MDPI, vol. 16(8), pages 1-19, April.

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