Energy Management of Hybrid Electric Commercial Vehicles Based on Neural Network-Optimized Model Predictive Control
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| Publicado en: | Electronics vol. 14, no. 16 (2025), p. 3176-3202 |
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| Autor Principal: | |
| Outros autores: | , , , , |
| Publicado: |
MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3244012081 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14163176 |2 doi | |
| 035 | |a 3244012081 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Hong Jinlong |u School of Automotive Studies, Tongji University, Shanghai 201804, China; hongjl@tongji.edu.cn (J.H.); yolo_yf@tongji.edu.cn (F.Y.); luoxi21@tongji.edu.cn (X.L.); chuhongqing@tongji.edu.cn (H.C.) | |
| 245 | 1 | |a Energy Management of Hybrid Electric Commercial Vehicles Based on Neural Network-Optimized Model Predictive Control | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. | |
| 653 | |a Energy management | ||
| 653 | |a Simulation | ||
| 653 | |a Commercial vehicles | ||
| 653 | |a Linear programming | ||
| 653 | |a Integer programming | ||
| 653 | |a Control algorithms | ||
| 653 | |a Collaboration | ||
| 653 | |a Deep learning | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Teaching methods | ||
| 653 | |a Neural networks | ||
| 653 | |a Electric vehicles | ||
| 653 | |a Optimization | ||
| 653 | |a Predictive control | ||
| 653 | |a State of charge | ||
| 653 | |a System dynamics | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Mixed integer | ||
| 653 | |a Real time | ||
| 653 | |a Energy consumption | ||
| 700 | 1 | |a Yang, Fan |u School of Automotive Studies, Tongji University, Shanghai 201804, China; hongjl@tongji.edu.cn (J.H.); yolo_yf@tongji.edu.cn (F.Y.); luoxi21@tongji.edu.cn (X.L.); chuhongqing@tongji.edu.cn (H.C.) | |
| 700 | 1 | |a Luo Xi |u School of Automotive Studies, Tongji University, Shanghai 201804, China; hongjl@tongji.edu.cn (J.H.); yolo_yf@tongji.edu.cn (F.Y.); luoxi21@tongji.edu.cn (X.L.); chuhongqing@tongji.edu.cn (H.C.) | |
| 700 | 1 | |a Xiaoxiang, Na |u Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK; xnhn2@cam.ac.uk | |
| 700 | 1 | |a Chu Hongqing |u School of Automotive Studies, Tongji University, Shanghai 201804, China; hongjl@tongji.edu.cn (J.H.); yolo_yf@tongji.edu.cn (F.Y.); luoxi21@tongji.edu.cn (X.L.); chuhongqing@tongji.edu.cn (H.C.) | |
| 700 | 1 | |a Tian Mengjian |u College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China | |
| 773 | 0 | |t Electronics |g vol. 14, no. 16 (2025), p. 3176-3202 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244012081/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244012081/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244012081/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |