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

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Pubblicato in:Electronics vol. 14, no. 16 (2025), p. 3176-3202
Autore principale: Hong Jinlong
Altri autori: Yang, Fan, Luo Xi, Xiaoxiang, Na, Chu Hongqing, Tian Mengjian
Pubblicazione:
MDPI AG
Soggetti:
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
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Abstract: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
Fonte:Advanced Technologies & Aerospace Database