Energy Management of Hybrid Electric Commercial Vehicles Based on Neural Network-Optimized Model Predictive Control

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Dades bibliogràfiques
Publicat a:	Electronics vol. 14, no. 16 (2025), p. 3176-3202
Autor principal:	Hong Jinlong
Altres autors:	Yang, Fan, Luo Xi, Xiaoxiang, Na, Chu Hongqing, Tian Mengjian
Publicat:	MDPI AG
Matèries:	Energy management Simulation Commercial vehicles Linear programming Integer programming Control algorithms Collaboration Deep learning Dynamic programming Teaching methods Neural networks Electric vehicles Optimization Predictive control State of charge System dynamics Energy efficiency Mixed integer Real time Energy consumption
Accés en línia:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripció
Resum:	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.
ISSN:	2079-9292
DOI:	10.3390/electronics14163176
Font:	Advanced Technologies & Aerospace Database