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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.11104 |
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| _version_ | 1866929281025703936 |
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| author | Park, Suyong Nguyen, Duc Giap Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok |
| author_facet | Park, Suyong Nguyen, Duc Giap Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle Park, Suyong Nguyen, Duc Giap Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok Systems and Control 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. |
| title | Deep Neural Network NMPC for Computationally Tractable Optimal Power Management of Hybrid Electric Vehicle |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2403.11104 |