Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle
<|reference_start|>Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle: This study presents a method for deep neural network nonlinear model predictive control (DNN-MPC) to reduce computational complexity, and we show its practical utility through its application in optimizing the energy management of hybrid electric vehicles (HEVs). For optimal power management of HEVs, we first design the online NMPC to collect the data set, and the deep neural network is trained to approximate the NMPC solutions. We assess the effectiveness of our approach by conducting comparative simulations with rule and online NMPC-based power management strategies for HEV, evaluating both fuel consumption and computational complexity. Lastly, we verify the real-time feasibility of our approach through process-in-the-loop (PIL) testing. The test results demonstrate that the proposed method closely approximates the NMPC performance while substantially reducing the computational burden.<|reference_end|>
@article{park2024deep,
    title={Deep Neural Network NMPC for Computationally Tractable Optimal Power
  Management of Hybrid Electric Vehicle},
    author={Suyong Park, Duc Giap Nguyen, Jinrak Park, Dohee Kim, Jeong Soo Eo,
  and Kyoungseok Han},
    journal={arXiv preprint arXiv:2403.11104},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11104},
    primaryClass={eess.SY cs.SY}
}