Surrogate-Driven Multi-Objective Predictive Control for Electric Vehicular Platoon

This paper proposes a surrogate-driven multi-objective predictive control (SMPC) strategy to address the dynamics uncertainty and multi-objective optimization issues of electric vehicular platoon (EVP). A surrogate-driven model is established with subspace identification to alleviate the adverse eff...

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Bibliographic Details
Published in:IEEE transactions on transportation electrification 2024, p.1-1
Main Authors: Wu, Yanhong, Zuo, Zhiqiang, Wang, Yijing, Han, Qiaoni, Li, Ji, Zhou, Quan, Xu, Hongming
Format: Article
Language:English
Subjects:
Batteries
Electric vehicular platoon
grey wolf optimization
Mathematical models
multi-objective optimization
Optimization
Predictive models
State of charge
surrogate-driven model predictive control
Transportation
Vehicle dynamics
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Summary:This paper proposes a surrogate-driven multi-objective predictive control (SMPC) strategy to address the dynamics uncertainty and multi-objective optimization issues of electric vehicular platoon (EVP). A surrogate-driven model is established with subspace identification to alleviate the adverse effects of uncertain dynamics for EVP. Then, a subspace predictor-based distributed surrogate-driven model predictive controller is developed for EVP. To mitigate conflicts among multiple optimization objectives involving driving safety, driving comfort and energy economy, a multi-objective cost function with the predictive sequence is designed. To this end, a grey wolf optimizer is suggested to guide the search towards diverse solutions, aiming to achieve globally optimal trade-offs among conflicting multiple objectives. In this way, the SMPC strategy is constructed, and its stability is theoretically proven. Finally, several experiments are carried out on a co-simulation vehicular platoon platform with the IPG-CarMaker software. The experimental results validate the effectiveness of the proposed SMPC strategy.
ISSN:2332-7782
2332-7782
DOI:10.1109/TTE.2024.3379590