A Learning-and-Tube-Based Robust Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

Hou, Zhuoran; Chu, Liang; Guo, Zhiqi; Hu, Jincheng; Jiang, Jingjing; Yang, Jun; Chen, Zheng; Zhang, Yuanjian

doi:10.1109/tiv.2023.3331268

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2024

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Cited by 3 publications

References 52 publications

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Optimum Energy Management Strategies for the Hybrid Sources-Powered Electric Vehicle Settings

Meydani,

Shahinzadeh,

Nafisi

et al. 2024

2024 28th International Electrical Power Distribution Conference (EPDC)

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Optimum Energy Management Strategies for the Hybrid Sources-Powered Electric Vehicle Settings

Meydani,

Shahinzadeh,

Nafisi

et al. 2024

2024 28th International Electrical Power Distribution Conference (EPDC)

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A dynamics model for Self-Propelled Modular Transporter*

Cai,

Wu,

et al. 2024

2024 IEEE Intelligent Vehicles Symposium (IV)

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Reliable Energy Optimization Strategy for Fuel Cell Hybrid Electric Vehicles Considering Fuel Cell and Battery Health

Ji,

Kamal,

Ghorbani

2024

Energies

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To enhance the fuel efficiency of fuel cell hybrid electric vehicles (FCHEVs), we propose a hierarchical energy management strategy (HEMS) to efficiently allocate power to a hybrid system comprising a fuel cell and a battery. Firstly, the upper-layer supervisor employs a fuzzy fault-tolerant control and prediction strategy for the battery and fuel cell management system, ensuring vehicle stability and maintaining a healthy state of charge for both the battery and fuel cell, even during faults. Secondly, in the lower layer, dynamic programming and Pontryagin’s minimum principle are utilized to distribute the necessary power between the fuel cell system and the battery. This layer also incorporates an optimized proportional-integral controller for precise tracking of vehicle subsystem set-points. Finally, we compare the economic and dynamic performance of the vehicle using HEMS with other strategies, such as the equivalent consumption minimization strategy and fuzzy logic control strategy. Simulation results demonstrate that HEMS reduces hydrogen consumption and enhances overall vehicle energy efficiency across all operating conditions, indicating superior economic performance. Additionally, the dynamic performance of the vehicle shows significant improvement.

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A Learning-and-Tube-Based Robust Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

Abstract: A learning-and-tube-based robust model predictive control strategy for plug-A learning-and-tube-based robust model predictive control strategy for plugin hybrid electric vehicle in hybrid electric vehicle PLEASE CITE THE PUBLISHED VERSION

Cited by 3 publications

References 52 publications

Optimum Energy Management Strategies for the Hybrid Sources-Powered Electric Vehicle Settings

Optimum Energy Management Strategies for the Hybrid Sources-Powered Electric Vehicle Settings

A dynamics model for Self-Propelled Modular Transporter*

Reliable Energy Optimization Strategy for Fuel Cell Hybrid Electric Vehicles Considering Fuel Cell and Battery Health

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