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A learning-and-tube-based robust MPC strategy for plug-in hybrid electric vehicle.pdf (1.66 MB)

A learning-and-tube-based robust model predictive control strategy for plug-in hybrid electric vehicle

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journal contribution
posted on 2023-11-14, 09:20 authored by Zhuoran Hou, Liang Chu, Zhiqi Guo, Jincheng HuJincheng Hu, Jingjing JiangJingjing Jiang, Jun YangJun Yang, Zheng Chen, Yuanjian ZhangYuanjian Zhang

The advancement of electromechanical coupling technologies has fostered the development of four-wheel-drive plug-in hybrid electric vehicles (4WD PHEVs) with multiple power sources, potentially enhancing travel efficiency. Moreover, sophisticated energy management strategies (EMSs) not only augment the efficacy of energy-saving control but also ensure adaptability under varied driving conditions. In this paper, a learning-and-tube-based robust model predictive control (LTRMPC) strategy is proposed for a 4-wheel-drive PHEV (4WD PHEV). The suggested strategy enhances the economic efficiency of the intricate powertrain while preserving control robustness across diverse driving scenarios. Firstly, instead of utilizing a mathematical state predictive model to mirror state changes, a novel state observer is proposed, which uses a deep learning technique named Gated Recurrent Unit (GRU). The novel state observer boasts an enhanced feature fusion ability, thereby reflecting state changes accurately within the predictive horizon. Secondly, to mitigate the negative effects of state observation errors on control outcomes, a tube-based cost function is integrated into the learning-MPC framework to restrain the state changes into a certain range to further reinforce the control robustness. Finally, a simulation evaluation and hardware-in-the-loop (HIL) test validate that the proposed method can improve economic performance across various lengths of the predictive horizon and the energy-saving capability is stable compared with other baselines, showcasing its promising performance.


State Scholarship Funding of CSC (Grant Number: 202206170067)

Changsha Automotive Innovation Research Institute Innovation Project-Research on Intelligent Trip Planning System of Pure Electric Vehicles Based on Big Data (Grant Number: CAIRIZT20220105)

Science and technology planning project in Yibin city (Grant Number: 2020GY001)

Science and technology planning project in Tianjin city (Grant Number: 20YFZCGX00770)



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Intelligent Vehicles


1 - 15


Institute of Electrical and Electronics Engineers (IEEE)


  • AM (Accepted Manuscript)

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Dr Jingjing Jiang. Deposit date: 13 November 2023