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

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 pape...

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Vydané v:Electronics (Basel) Ročník 14; číslo 16; s. 3176
Hlavní autori: Hong, Jinlong, Yang, Fan, Luo, Xi, Chu, Hongqing, Tian, Mengjian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 09.08.2025
Predmet:
Analysis
Collaboration
Commercial vehicles
Control algorithms
Deep learning
Dynamic programming
Electric vehicles
Embedded systems
Energy consumption
Energy efficiency
Energy management
Energy management systems
Energy use
Hybrid vehicles
Integer programming
Linear programming
Mixed integer
Neural networks
Optimization
Predictive control
Real time
Simulation
State of charge
System dynamics
Teaching methods
United States
ISSN:2079-9292, 2079-9292
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics14163176